Research on Cloud-Edge-Device Collaborative Intelligent Monitoring System of Grinding Wheel Wear State for High-Speed Cylindrical Grinding of Bearing Rings

Abstract

Aiming at the problems of grinding wheel wear during high-speed cylindrical grinding, communication delays, and slow response during data acquisition, processing, and system operation, an intelligent online monitoring technology frame for CNC manufacturing units is proposed, incorporating a real-time-perception grinding mechanism and a cloud-edge device. Based on the grinding data and grinding wheel wear mechanism, a monitoring model using multi-sensor information fusion is constructed to assess the grinding wheel wear state. In addition, edge data acquisition and online monitoring software have been developed to improve the speed of data transmission and processing. Finally, based on the proposed framework, a cloud-edge device collaborative intelligent monitoring system for assessing grinding wheel wear during high-speed cylindrical grinding of bearing rings is constructed. It improves the grinding quality and efficiency, reduces the grinding cost, and incorporates remote control functionality.

1. Introduction

High-end bearings have the characteristics of high speed, high reliability, and low noise. They can usually work in extreme environments. Therefore, they have many applications in significant manufacturing equipment [1]. The outer surface of the bearing ring is the working face while in service. Its surface quality is directly related to the service performance. Grinding is a critical process to ensure the surface quality of bearing rings [2]. However, the grinding wheel wear state directly relates to the grinding efficiency and quality. It gradually wears away with the grinding process affected by mechanical, physical, and chemical factors. The abrasive particles gradually lose their cutting ability when the grinding wheel is seriously worn. If the grinding continues, it may cause grinding burn or chatter [3,4]. The grinding quality is difficult to guarantee without the wheel being dressed or replaced in a timely manner. However, dressing is usually carried out in advance when the grinding wheel has not reached the limit of its working life. Premature and frequent replacement or dressing of grinding wheels will reduce the continuity of production, increase downtime, and affect efficiency and cost. According to relevant statistics, machine tools and labor costs caused by tool wear account for 20% and 38% of manufacturing costs, respectively [5]. It can be seen that manufacturing costs can be saved, and efficiency can be improved if the grinding wheel is used accurately. Therefore, monitoring the grinding wheel wear state is critical to improving productivity and grinding quality.

The physical reality of grinding wheel wear is difficult to truly reflect through modeling and simulation [6]. Modeling does not provide a reliable basis for decisions about the grinding wheels’ selection, replacement, and dressing. However, sensors can collect machining process information in real time to establish a relationship model between the signal and the grinding wheel wear state. Complex grinding mechanism analyses and modeling processes are avoided to a certain extent [7,8]. Many studies have been conducted by scholars on the online monitoring of machining process status [9]. Tian et al. [10] combined the power signal with the grinding conditions to optimize the grinding process and develop a portable energy consumption monitoring system. Guo et al. [11] used acoustic emissions (AE) to monitor the wear state of a diamond grinding wheel. They found that the wear state was closely related to the effective value, variance, and energy coefficient. Wang et al. [12] realized the condition monitoring of cylindrical transverse precision grinding by collecting signals in situ with AE sensors. Li et al. [13] extracted feature information based on the spindle current signal by combining supervised learning with unsupervised learning to predict the degree of tool wear. However, prediction accuracy is not high due to the reliance on a single signal. In current multi-sensor online monitoring, problems such as information redundancy and insufficient data fusion are due to the relatively independent sensors, resulting in poor real-time performance and low prediction accuracy. Therefore, it is essential to configure sensor resources according to the sensing target to integrate the data at all levels fully.

The grinding process generates a large amount of real-time data by deploying and applying many sensors. However, these data have many categories, high complexity, and uneven quality, leading to tremendous pressure on network, computing, and storage resources [14]. It is challenging to meet the needs of real-time online monitoring of current grinding. Li Hai et al. [15] proposed a resource selection method of machine tools based on multi-criteria decision making for cloud manufacturing. However, only the application scope of the manufacturing cloud platform has been expanded, and the demand for real-time response on the device side has not been resolved. With the emergence of edge computing technology, cloud-edge devices have been proposed as a new data computing paradigm [16]. The basic idea is to decentralize some of the computing functions of the cloud end to the edge end to shorten the communication link of data and improve computing performance [17]. Cloud-edge device collaborative techniques have been developed and applied in more and more fields. Ding et al. [18] proposed a cloud-edge device collaboration framework to provide cognitive services with long durations and fast responses through shallow convolutional neural network models. Li et al. [19] designed a cloud-edge-end collaborative architecture suitable for machine tool fault diagnosis to meet the high real-time requirements of actual production. Based on the improved DSCNN-GAP algorithm, Tang et al. [20] proposed a fast cloud-edge collaborative bearing fault diagnosis method. At present, there are few studies on the integration of cloud-edge device collaboration into production [21]. In addition, research on cloud-edge device collaboration for online monitoring and controlling intelligent CNC machine tools is even rarer. The scale of data on the machine tool device end is gradually expanding. More than 50% of the data must be stored, calculated, and analyzed on the edge end to have real-time response capabilities [22]. Therefore, it is urgent to establish an online monitoring system based on cloud-edge device collaboration.

This paper proposes a cloud-edge device collaborative intelligent monitoring framework for grinding wheel wear state during high-speed cylindrical grinding of bearing rings. Combined with the online monitoring method, a cloud-edge device collaborative intelligent monitoring system is developed through hardware (such as sensors, PLCs, and network cards) and programming languages (such as Java, LabView, MATLAB, and MySQL databases) to realize grinding wheel wear state recognition. The structure of this paper is as follows: In the second section, the framework of an intelligent monitoring technology system based on cloud-edge device collaboration is proposed. The third section introduces the grinding wheel wear condition monitoring method based on multi-sensor information fusion and verifies its superiority. The relationship between signal and grinding wheel wear is analyzed. The fourth section introduces the architecture and development application of the cloud-edge device collaborative intelligent monitoring system. Finally, the work carried out is summarized and discussed.

2. Cloud-Edge Device Collaborative Monitoring System Framework for Grinding Wheel Wear State

2.1. Overall System Framework

2.1.1. Demand Analysis

A cloud-edge device collaborative intelligent monitoring system must accurately monitor the grinding wheel in real time to guarantee the efficiency and quality of high-speed cylindrical grinding. Therefore, analyzing the demand for cloud-edge device collaborative intelligent monitoring systems is necessary. From the actual needs, its demand can be expressed as follows:

(1)

Monitoring model with high accuracy and reliability

Accurate and reliable online monitoring is conducive to the production personnel grasping the wear state of the grinding wheel intuitively and quickly so as undertake dressing or replacement in time. However, grinding wheel wear in high-speed cylindrical grinding is complex. The factors affecting monitoring include the grinding process parameters, grinding wheel materials, workpiece materials, grinding fluid, and temperature. Therefore, combining the grinding wheel wear mechanism with the artificial intelligence algorithm is necessary to construct a more accurate and reliable online monitoring model for grinding wheel wear.

(2)

Online monitoring system with fast response

Massive amounts of data are generated due to the difference in signal sources, the variety of sampling strategies, and the high sampling frequency in high-speed cylindrical grinding. The terminal equipment communicates with the cloud center on a large scale and frequently, resulting in network congestion. The processing and analysis of massive data aggravate the cloud’s operation burden, making the online monitoring system respond too slowly. Therefore, the communication delay of the single cloud-end online monitoring system is high, and the equipment response is slow. Some operation instructions in the device end cannot be feedback-regulated in a timely manner. Therefore, an online monitoring system with a fast response capability dramatically impacts the quality and efficiency of high-speed grinding of bearing rings.

(3)

Reasonable allocation of computing storage resources

Currently, the manufacturing data of CNC machine tools in the production site are usually uploaded to the cloud end. However, due to the diversity of data sources and the large amount of data, the storage pressure in the cloud end is high, and the data coordination is difficult, so the factors affecting the machining cannot be analyzed in time. The real-time performance of data processing needs to be further improved, and the problem of information islands is prominent. There is a lack of adequate information interaction and collaboration between systems [23]. The hardware and software resources are repeatedly constructed, resulting in resource waste. The complex and lengthy data transmission link reduces the efficiency of data interaction. It is difficult to match the real-time requirements of grinding wheel wear condition monitoring in high-speed grinding. The centralized data management adds to the storage pressure and computing power burden. The unnecessary repeated handling of large amounts of data increases the network load and the transmission delay of data [24]. Therefore, the rational allocation of cloud-edge device computing storage resources is conducive to rapid responses and reducing the waste of computing storage resources in online monitoring systems.

2.1.2. Cloud-Edge Device Collaborative Monitoring Architecture

In order to solve the problems mentioned in demand analysis, the cloud-edge device collaboration technology is applied to develop a grinding wheel wear state monitoring system for the high-speed grinding of a bearing ring. The sensing node in the CNC grinder is deployed for dynamic data acquisition. The edge server is deployed next to the CNC grinder. The edge software is developed for real-time grinding data analysis, feature extraction, and online monitoring model training/application. Data mining of long-period, non-real-time, and global CNC grinder data is carried out in the cloud. Digital management and intelligent control of grinding and the CNC grinder are carried out in the cloud end. The online monitoring grinding model of the grinding wheel wear state is established by studying the wear mechanism of a high-speed grinding wheel and analyzing the sensing signal. The real-time-perception grinding mechanism–cloud-device end collaboration intelligent online monitoring technology framework for CNC manufacturing units is shown in Figure 1.

(1)

Machine Tool Device End

The machine tool device end mainly includes a CNC grinder and various heterogeneous sensors (vibration, AE, power sensors, etc.). It interacts with edge servers and cloud servers through communication protocols. Accurate perception is critical to the system’s intelligent identification and control ability. Real-time data acquisition is realized through the OPC UA communication interface, the PLC, and sensors during the high-speed grinding of bearing rings. Then, it is transmitted to the edge and cloud through a 4G/5G network, bus interface, and edge intelligent gateway.

(2)

Edge End

The edge end mainly deals with the real-time demand business of grinding. The grinding wheel wear state is monitored online through data cleaning, data processing, data storage, online monitoring model construction, and edge computing. Then, the real-time state is transmitted to the cloud and device end to guide operators or managers to make decisions. The data analysis is mainly performed by the edge server and assisted by the cloud server to reduce the pressure of uplink data traffic on network communication and improve the real-time performance of information interaction feedback and remote monitoring. The trained recognition model is deployed at the edge end. The real-time data are used as an input, and the identified grinding wheel wear state is used as an output.

(3)

Cloud End

The cloud end is equipped with a data processing server and a data visualization interface. The prediction results of the online monitoring model of the grinding wheel wear state and the processed real-time sensing data are provided to the cloud platform by the edge end, and a visual interface is constructed to realize remote monitoring. The machine tool device end uploads the perceived CNC grinder attribute, grinding operation, machine tool operation, and other data to the cloud end for processing and management applications through the OPC UA communication protocol.

(4)

Data

Data serve as a bridge for cloud-edge device collaboration. They connect data acquisition, integration, analysis and decision-making, feedback control, and other links. Grinding state data collected online during grinding, including vibration, AE, and power, can characterize the grinding wheel wear state. These kinds of data usually have a high sampling frequency and are extensive, but real-time requirements are very high. The value of the data is inversely proportional to the delay generated during transmission and calculation. The grinding state data are distributed to the nearest edge server for storage, processing, analysis, and grinding state recognition to improve the interaction efficiency. Equipment state data usually refer to the operation data of the grinder, grinding parameters, and some historical data from the offline state. These kinds of data are small in number and have a low sampling frequency, but they have many categories and low real-time requirements. Therefore, the number of device states is distributed directly to the cloud for centralized storage and use.

2.2. Key Technology of Grinding Wheel Wear State Monitoring with Multi-Sensor

There are two types of monitoring methods, as shown in Figure 2. Direct monitoring refers to the equipment, such as an optical microscope or CCD, which captures the state information of the grinding in situ to analyze their state changes. Direct monitoring can capture status information with high accuracy. However, it needs to suspend the processing, which is time-consuming and affects the machining efficiency. Indirect monitoring refers to realizing online monitoring by establishing the mapping relationship between physical signals (such as vibration, AE, and power) and the grinding state. It can monitor the state of the machining process without affecting processing. The condition monitoring of multi-sensor information fusion is realized through key technologies such as real-time perception, signal processing, data analysis, multi-sensor information fusion technology, quantitative evaluation of grinding wheel wear state, and online identification.

(1)

Real-time sensing

Grinding wheel wear condition monitoring mainly obtains grinding state data through machine tool electrical circuits and external sensors (such as vibration, power, and AE). Real-time and accurate information collection of the grinding state is the first step in identifying the wear state of the grinding wheel.

(2)

Signal processing and analysis

Signal processing is essential to obtaining the state information in grinding. The computer cannot directly identify the grinding wheel wear state through the original sensing signal, which is a waveform signal. Therefore, combining multiple signal processing and statistical methods to extract signal features with a high correlation is necessary to characterize the wear state. Then, feature optimization and dimension reduction are carried out to train the online monitoring model.

(3)

Multi-sensor information fusion technique

Single-sensor monitoring has low stability due to its single information source and vulnerability to environmental impact. It cannot accurately reflect changes in the machining state [25]. Multi-sensor monitoring has more abundant information sources and higher reliability due to the complementarity and collaboration of multi-sensor information. Therefore, multi-sensor information fusion technology is vital for realizing online monitoring of grinding wheel wear states with high accuracy and reliability. As shown in Figure 3, the multi-sensor information hierarchical fusion method in this paper divides information fusion into three levels: data-level fusion, feature-level fusion, and decision-level fusion. Data-level fusion belongs to the underlying fusion, which has the advantages of a large amount of information and no data loss. Feature-level fusion improves the speed of data transmission and processing by compressing information. It provides further information for recognition analysis. Decision-level fusion belongs to the top-level information fusion. It obtains decision information by associating information sources and outputs it as the result of joint inference.

(4)

Quantitative evaluation

Quantitative evaluation of grinding wheel wear state and accurate division of different wear stages are prerequisites for online monitoring. When quantifying the wear state of the grinding wheel, it is necessary to consider measurement accuracy and convenience [26].

Table 1. Different quantification methods of grinding wheel wear state.

Method	Characteristics	Advantage	Inferiority
Laser measurement	Measuring the height change of abrasive wear.	High precision and reliability.	It is expensive and inconvenient to detect.
Visual imaging	The surface morphology of the grinding wheel is obtained by optical imaging.	In-situ detection, high reliability, and cost-effective.	It has high requirements for installation space and accuracy.
Re-engraving	The worn grinding wheel is ground into thin metal sheets, and its profile is reproduced.	The principle is simple.	The precision of operation is very high, and the error rate is high.

gives different quantitative detection methods for grinding wheel wear state. The shape and distribution of abrasive particles on the surface of the grinding wheel are random. Therefore, evaluating and quantifying grinding wheel wear is more complicated than milling and turning. Consequently, it is necessary to research the wear mechanism of the grinding wheel to formulate an accurate classification standard.

(5)

Online identification

Constructing the recognition model of the grinding wheel wear state is a crucial step in online monitoring. Establishing the mapping relationship between the sensing signal and the grinding state is essential. The online identification model can be realized by statistical and artificial intelligence methods [27]. It is crucial to grasp the machining law further by studying the grinding mechanism and establishing the analytical or empirical model of the machining state [28]. Therefore, the online identification model must fuse the grinding mechanism with the data. Guided by the grinding mechanism model, the nonlinear mapping of the model is used to fit the relationship between the sensing signal and the grinding wheel wear state.

2.3. Cloud-Edge Device Collaboration Mechanism for Grinding Wheel Wear State Monitoring

Cloud-edge device collaboration enhances performance when processing massive quantities of data. It reduces task latency by collaborating with the resources of terminal devices, edge servers, and cloud data centers. It has higher real-time performance, stability, and safety to meet real-time recognition requirements, as shown in Figure 4.

Importing and exporting massive quantities of data to the cloud server is very complicated. Problems such as insufficient bandwidth and significant latency affect the interaction between the cloud end and the machine tool device end. The above issues will be solved well by edge computing technology. The edge gateway and server are deployed on the side of the data source to provide edge-intelligent services [29], which meet the requirements of online monitoring and rapid response. Edge computing is an extended concept of cloud computing. It has the advantages of low delay and strong real-time ability due to its location between the cloud end and the machine tool device end [30]. Therefore, online monitoring with fast response, high accuracy, and remote control can be realized through the cooperation of cloud and edge computing.

Real-time acquisition of grinding and equipment status data through different sensing entities and transmission data is the basis for constructing an online monitoring system for grinding wheel wear status. The machine tool device end is responsible for data acquisition, which provides data support for cloud-edge device collaborative intelligent monitoring systems. The edge end is responsible for grinding state data storage, data cleaning, analysis, and calculation. The processed data are transmitted to the trained online monitoring model in the edge monitoring software to realize online monitoring. The monitoring results and processed data are fed back to the cloud end. The cloud data platform performs data processing, essential management, and visualization and can send decision-making instructions to the machine tool device end. The cloud-edge device collaboration mechanism interacts as follows:

(1)

The machine tool device transmits the grinding state data to the edge end for storage in real time. At the same time, the device status data are transmitted to the cloud end to manage the grinding performance and operation remotely.

(2)

After the data processing and analysis in the edge end, the grinding wheel wear state will be identified.

(3)

The real-time monitoring results will be transmitted to the cloud and the machine tool device end, and the operation instructions will be sent simultaneously.

(4)

The machine tool device end receives alarms and decision-making instructions from the cloud and edge ends.

3. Monitoring Method Based on Multi-Sensor Information Fusion

The following steps are usually required to monitor the grinding wheel wear status online. (1) The construction of a data acquisition platform. (2) The signal features are obtained by signal processing. (3) The features are optimized and reduced. (4) The grinding wheel wear state quantitative evaluation. (5) The recognition model is constructed.

3.1. Data Acquisition Platform and Grinding Experiment

The experiment was carried out on the CNC8325 CNC ultra-high-speed compound grinding machine, as shown in Figure 5. The workpiece was a GCr15 bearing ring, and the vitrified CBN grinding wheel (Zhengzhou Three Mills, Zhengzhou, China) was used. The AE, vibration, and power signals were collected simultaneously during the grinding experiment. According to our team’s previous research [31], the AE sensor should be installed in the Z-axis direction of the tailstock. The vibration sensor was placed in the X-axis direction of the headstock, and the power meter was directly connected to the grinder’s electrical cabinet. The acoustic emission acquisition system comprised the NI 6351 acquisition card, PAS preamplifier, and W500 broadband acoustic emission sensor. The vibration signal was collected by the IEPE acceleration sensor and the ANVAT data acquisition instrument produced by Hangzhou Yiheng Technology Co., Ltd. (Zhengzhou Three Mills, China) The power meter used was YOKOGAWA’s WT330 digital power meter (Tokyo, Japan) with a maximum sampling frequency of 10 HZ. Among them, the AE sampling frequency is 1 MHz, the vibration signal was 6400 Hz, and the power signal was 10 Hz. In the experiment, the grinding wheel speed V_s was 120 m/s, the workpiece speed V_w was 50.24 m/min, and the grinding depth a_p was 30 μm. The cumulative material removal volume A_mrv was taken as a variable until the grinding quality cannot be guaranteed.

3.2. Grinding Wheel Wear State Identification Model Based on Multi-Sensor Information Fusion

The process of the multi-sensor information fusion method is shown in Figure 6.

The data-level fusion model was constructed by signal preprocessing, processing, feature extraction, and optimization dimension reduction technology. The statistical features of time-domain signals are usually calculated to characterize the change in the grinding wheel wear state.

In addition, signal spectrum analysis is essential. The correlation between the signal frequency and the grinding wheel wear states is analyzed through the power spectral density (PSD) [32]. Wavelet packet decomposition (WPD) is adopted for the AE signal. WPD analyzes the energy value of each frequency band and selects the best to extract sensitive features [33]. The low-pass filter coefficient h(k) and high-pass filter coefficient g(k)g are as follows:

$$\left\{\begin{array}{c}h(k)={\displaystyle \frac{1}{\sqrt{2}}}\varphi ({\displaystyle \frac{t}{\sqrt{2}}}),\varphi (t-k)\\ g(k)={\displaystyle \frac{1}{\sqrt{2}}}\psi ({\displaystyle \frac{t}{\sqrt{2}}}),\psi (t-k)\end{array}\right.$$

(1)

Here, ϕ(t) is a scaling function, ψ(t) is a wavelet function, satisfying g(k) = (−1)^kh(1 − k), and h and g are orthogonal.

Let $U_{j+1}^{2n}$ be the scale space and $U_{j+1}^{2n+1}$ be the wavelet space. The WPD is

$$U_j^n=U_{j+1}^{2n}\oplus U_{j+1}^{2n+1},j\in Z,n\in Z^{+}$$

(2)

according to the wavelet packet function family defined by the following two-scale equation:

$$\left\{\begin{array}{c}{u}_{2t}(t)=\sqrt{2}{\displaystyle {\sum }_k{u}_n(2t-k)h(k)}\\ u_{2t+1}(t)=\sqrt{2}{\displaystyle {\sum }_k{u}_n(2t-k)g(k)}\end{array}\right.$$

(3)

Equation (3) is a wavelet packet function determined by the basis function $u_n=\varphi (t)$.

The decomposition coefficient and reconstruction relationship algorithm are:

$$d_j^i(n)={\displaystyle {\sum }_k{d}_{j+1}^{2i-1}}(k)h(k-2n)+{\displaystyle {\sum }_k{d}_{j+1}^{2i}}(k)g(k-2n)$$

(4)

Here, $d_j^i(n)$ denotes the wavelet packet coefficients of the i-th node in the j-th layer.

For the vibration signal, the Hilbert–Huang Transform (HHT) is used. Firstly, the empirical mode decomposition (EMD) is carried out to convert the signal into linear and steady-state signals. Then, the intrinsic mode function (IMF) obtained by EMD is used as the input of HT. This ensures the Hilbert Transform (HT) prerequisite. For the vibration signal x(t), its HT is H[x(t)].

z(t) = x(t) + H[x(t)]

(5)

Here, z(t) is the analytic signal, x(t) is the real part of the complex signal, and H[x(t)] is the imaginary part of the complex signal.

The essence of H[x(t)] is a 90° phase shifter:

$$H[x(t)]={\displaystyle \frac{1}{\pi }}{\displaystyle {\int }_{-\infty }^{+\infty }\frac{x(\tau )}{t-\tau }}d\tau$$

(6)

Since H[x(t)] is the convolution of x(t) and 1/πt:

$$H[x(t)]=\left|H[x(t)]\right|e^{j\phi (\omega )}=\left\{\begin{array}{c}-j\omega >0\\ +j\omega <0\end{array}\right.$$

(7)

By introducing Euler’s formula e^jωn = cos ωn + j sin ωn:

$$\phi (\omega )=\left\{\begin{array}{c}-{\displaystyle \frac{\pi }{2}},\omega >0\\ +{\displaystyle \frac{\pi }{2}},\omega <0\end{array}\right.$$

(8)

It can be seen that when the frequency is greater than 0, the phase shifts to the left by 90°. On the contrary, if it is less than 0, the phase moves to the right 90°, which explains the analysis principle of HT.

After signal processing, statistical features are extracted in the time, frequency, and time-frequency domains to construct the original feature subset (AE_F and V_F) of AE and vibration signals. The Pearson correlation coefficient r is used to calculate the correlation between the features and grinding wheel wear to extract the signal features with a high correlation. The value range of r and correlation degree are shown in

Table 2. Correlation coefficient value and its correlation degree.

|r|	[0, 0.3]	(0.3, 0.5]	(0.5, 0.8]	(0.8, 1]
correlative degree	No linear correlation	Low correlation	Significant correlation	High correlation

. The signal features with |r| ≥ 0.6 were selected to construct the preferred feature subsets (AE_P and V_P). Since each signal feature reflects the grinding wheel wear state to varying degrees, the effective information overlaps. Principal components analysis (PCA) dimension reduction is used to transform the multi-dimensional indicators into a few comprehensive indicators to reduce information redundancy [34]. The optimal feature subset (AE_PCA and V_PCA) of AE and vibration signals is constructed using the output low latitude feature subset.

As a feature-level fusion model, a back propagation neural network (BPNN) realizes preliminary recognition of the grinding wheel wear state. Two nonlinear variables can establish a mapping relationship through the BPNN [35]. d_k is the expected output value, o_k is the actual output value, w_jk is the connection weight between the input and the hidden layer, and v_ij is the connection weight between the hidden and the output layer. The activation function selects the unipolar S-type function f(x) = 1/(1 + e^−x), and the network error is calculated as follows:

$$E={\displaystyle \frac{1}{2}}{(d-o)}^2={\displaystyle \frac{1}{2}}{\displaystyle {\sum }_{k=1}^l(d_k-o_k}{)}^2$$

(9)

The hidden layer error E₁ and input layer error E₂ is

$$\left\{\begin{array}{c}\begin{array}{l}{E}_1={\displaystyle \frac{1}{2}}{\displaystyle {\sum }_{k=1}^l(d_k}-f(ne{t}_k){)}^2\begin{array}{cc}\begin{array}{ccc}& & \end{array}& \begin{array}{cc}\begin{array}{cc}& \end{array}& \end{array}\end{array}\\ \begin{array}{cc}& \end{array}={\displaystyle \frac{1}{2}}{{\displaystyle {\sum }_{k=1}^l(d_k-f({\displaystyle {\sum }_{j=0}^m{w}_{jk}}{y}_j))}}^2\end{array}\\ \begin{array}{l}{E}_2={\displaystyle \frac{1}{2}}{\displaystyle {\sum }_{k=1}^l(d_k}-f({\displaystyle {\sum }_{j=0}^m{w}_{jk}f(ne{t}_j)}){)}^2\\ \begin{array}{cc}& \end{array}={\displaystyle \frac{1}{2}}{{\displaystyle {\sum }_{k=1}^l(d_k-f({\displaystyle {\sum }_{j=0}^m{w}_{jk}f({\displaystyle {\sum }_{j=0}^m{v}_{ij}}}{x}_i)))}}^2\end{array}\end{array}\right.$$

(10)

The hidden layer output y_i and output layer output o_k is

$$\left\{\begin{array}{c}{y}_i=f(ne{t}_j)=f({\displaystyle {\sum }_{i=1}^n{v}_{ij}{x}_i+a_j})\\ o_k=f(ne{t}_k)=f({\displaystyle {\sum }_j^m{w}_{jk}{x}_i+b_k})\end{array}\right.$$

(11)

Among them, a_j is the threshold corresponding to the hidden layer, and b_k is the threshold corresponding to the output layer.

According to Equation (10), the error E is a function of w_jk and v_ij. The network error can be reduced by adjusting the corresponding weights. The network weight is inversely proportional to the gradient direction of the network error, that is

$$\left\{\begin{array}{c}△w_{jk}=-\eta {\displaystyle \frac{\partial E}{\partial w_{jk}}},j=0,1,2,\dots ,m;k=1,2,\dots ,l\\ △v_{ij}=-\eta {\displaystyle \frac{\partial E}{\partial v_{ij}}},i=0,1,2,\dots ,n;j=1,2,\dots ,m\end{array}\right.$$

(12)

Among them, η is the learning rate.

Decision-level fusion fuses the recognition results of BPNN. Dempster–Shafter evidence theory (D-S) can comprehensively integrate multi-source information to evaluate [36]. The D-S synthesis method is used to obtain the comprehensive evaluation results of the grinding wheel wear state by taking the output of each sub-BPNN model as evidence. When constructing the decision fusion model, the recognition framework comprises different categories of grinding wheel wear. The basic probability distribution is the reliability of the wear degree of the grinding wheel. The belief function and the likelihood function are the specific probability sets representing each proposition.

Frame of discernment: Let Θ be a non-empty and finite set composed of many mutually exclusive propositions, and the set formed by all categories is called the power set of Θ, expressed as 2^Θ, and any grinding wheel wear proposition or combination is its subset.

Basic probability assignment (BPA) is a function under the recognition frame mapping m: 2Θ → [0, 1], which satisfies the following formula:

$$\left\{\begin{array}{c}m(\varnothing )=0\\ {\displaystyle {\sum }_{A\subseteq \mathsf{\Theta }}m(A)=1}\end{array}\right.$$

(13)

Among them, A represents the basic proposition, m (A) is the degree of support for the basic proposition A, and the focal element refers to the proposition of m (A) > 0.

The belief function (Bel) denotes the degree of trust in the basic proposition. The definition of Bel under Θ is as follows:

$$Bel(A)={\displaystyle {\sum }_{B\subseteq A}m(B)}$$

(14)

The plausibility function (Pl) denotes the degree of trust in the basic proposition.

$$pl(A)={\displaystyle {\sum }_{B\cap A\ne \varnothing }m(B)}$$

(15)

Among them, A and B are the basic propositions, and A, B$\subseteq$Θ. The belief function is the lower limit of the support interval for any proposition. The likelihood function is the upper limit of the support interval for any proposition.

Combination rule: Suppose the two independent basic probability assignment functions on Θ are m₁ and m₂, respectively. The basic propositions A, B, and C are sub-propositions on Θ. Then, the D-S composition rule is

$$m(A)=\left\{\begin{array}{c}{\displaystyle \frac{{\displaystyle {\sum }_{B\cap C=A}{m}_1(B)m_2(C)}}{1-k}},A\ne \varnothing \\ 0,A=\varnothing \end{array}\right.$$

(16)

where k is the evidence conflict factor and $k={\displaystyle {\sum }_{B\cap C=\varnothing }{m}_1(B)·m_2}(C)$. When 0 < k < 1, the basic probability distribution of each proposition can be fused respectively to realize the fusion of single recognition results and form a comprehensive evaluation system.

3.3. Experimental Analysis and Model Verification

3.3.1. Relationship between the Sensing Signal and Grinding Wheel Wear

The surface morphology of the grinding wheel is observed by a GP-660 V electron microscope, and the actual wear degree of the grinding wheel is judged by the abrasive wear plane area rate R_area, as shown in Figure 7 and Figure 8.

Taking the A_mrv as the abscissa, the R_area, the material removal power, and the surface roughness Ra as the ordinate, the change curve of abrasive wear information for the grinding wheel is drawn, as shown in Figure 9. With the increase of the A_mrv, the R_area, Ra, and the material removal power gradually increase. The power signal agrees with the change in grinding wheel wear. In the initial stage of grinding wheel wear, the abrasive breakage rapidly increases the wear plane, and the grinding force rapidly increases. So, the grinding removal power required also increases quickly. The abrasive is relatively stable in the stable wear stage. It mainly occurs during attrition wear. At this time, the abrasive wear plane increases slowly, and the required grinding removal power also increases gradually. However, in the severe wear stage, there is almost no effective grinding edge, resulting in breakage of the bond bridge. Therefore, a large number of abrasive grains fall off. The contact area between the grinding wheel and the workpiece increases, and the material removal mechanism changes from shearing to plowing and sliding. More power is needed to remove the same A_mr. It can be found that the grinding power signal increases linearly with the increase of the R_area. According to the grinding information curve, the grinding wheel wear stages are divided. When 0 < R_area < 16%, the grinding wheel is in the initial wear stage. When 16% < R_area < 29%, the grinding wheel is in the stable wear stage. When R_area > 29%, the grinding wheel has been severely worn.

After defining the standard of grinding wheel wear, the AE and vibration signals are analyzed to explore the relationship between grinding wheel wear and sensing signals. The incomplete lobes were removed by chopping, and 0.1-s AE and 0.5-s vibration signals were intercepted for analysis. During grinding, the sensor signal may be polluted by various noises, which reduces the signal-to-noise ratio. Therefore, the sensor signal is filtered by the wavelet threshold method. The AE and vibration signals in the time domain are shown in Figure 10. The signal amplitude increases with the grinding wheel wear. The way of removing the material has also changed from the original chip formation to sliding friction and plowing. The number of effective grains in the grinding will increase, and the single grain’s maximum undeformed cutting thickness will increase. However, in the frequency domain, the signal amplitude change is not apparent, as shown in Figure 11. However, the movement of the frequency center of gravity with the change of grinding wheel wear is more present in the PSD analysis.

The AE signal is decomposed by three layers of wavelet packets to explore the relationship between the grinding wheel wear and the sensing signal further, as shown in Figure 12. After WPD, it was found that the energy of the second node (3, 1) and the third node (3, 2) accounted for 56.2552% and 33.0805% of the total energy, respectively. It can be seen that the second and third nodes carry the primary information of the grinding. Therefore, the wavelet domain signals of the two nodes are reconstructed separately. After extracting the PSD features of the reconstructed AE signal, it can be found that the change curve is similar to the trend of grinding wheel wear. Among them, the features of centroid frequency, mean frequency, and pulse factor are in good agreement with the wear trend of the grinding wheel, indicating that these features are closely related to the wear state of the grinding wheel, as shown in Figure 13. We find that these signal characteristics have a positive correlation with grinding wheel wear, which is consistent with the above quantitative evaluation index. Therefore, it can be explained that these signal characteristics can characterize the grinding wheel wear well during the high-speed grinding process of the outer cylindrical bearing ring. They are valid data for constructing the input matrix of the online monitoring model.

The Hilbert–Huang time-frequency analysis of the vibration signal is carried out. Firstly, the vibration signal is decomposed into eight IMFs and one residual component by EMD. Based on the IMF energy ratio of the EMD algorithm, IMF1 and IMF2, which account for 28.56% and 71.28% of the total energy, are selected. Then, the IMF implements HT. The frequency distribution of IMF is mainly concentrated in the low-frequency band of 0~1500 HZ. The marginal spectrum of the signal is obtained further to analyze the signal information of IMF1 [37]. The marginal spectrum of the vibration signal is shown in Figure 14. The peak value of the marginal spectrum frequency of the vibration signal gradually moves to the left with the grinding wheel wear. In addition, the amplitude also changes significantly when the wear is severe. When the grinding wheel is seriously worn, the grinding can no longer be carried out, and the material removal ability is significantly reduced. This time, the sliding friction between the grinding wheel abrasive and the workpiece and the plastic deformation of the abrasive are more likely to produce low-frequency signals. After further extracting the features of the marginal spectrum, it is found that it is in good agreement with the wear trend of the grinding wheel, as shown in Figure 15. The strong correlation between these signal characteristics and the grinding wheel wear can be used to construct the input matrix of the on-line monitoring model.

The sensing signal is closely related to the removal mode of the material. When the wear state of the grinding wheel changes, the removal and contact mode change, which leads to a change in the sensing signal. Grinding wheel abrasive attrition wear is mechanical wear caused by the relative sliding friction between the grain and the workpiece. This wear is gradual, so the sensing signals, such as power, vibration, and AE, gradually change. There is a high correlation between the sensing signals. Therefore, the state of the grinding wheel wear process can be characterized by power, vibration, and AE signals.

3.3.2. Verification of Condition Monitoring Method

After signal feature extraction, AE_F contained 50-dimensional original AE signal features, while V_F contains 54-dimensional original vibration signal features. The r between the signal features and the grinding wheel wear index was calculated. The signal features of the time, frequency, and time-frequency domain with |r| greater than 0.6 were selected to construct the feature selection subset. Among them, the AE signal preferred 24-dimensional features to build the feature-preferred subset AE_P, and the vibration signal preferred 29-dimensional features to construct the feature-preferred subset V_P. Some preferred features are shown in

Table 3. Some examples after optimization of sensor signal features.

Signal	Time Domain Feature	Correlation	Frequency Domain Feature	Correlation	Time-Frequency Domain Feature	Correlation
AE signal	Root mean square	69.5%	Pulse factor	84.8%	Kurtosis factor	82.8%
	Kurtosis factor	68.0%	Crest frequency	76.9%	Crest frequency	80.8%
	Peak-peak value	63.2%	Mean frequency	66.8%	Frequency variance	76.2%
	Variance	61.6%	Frequency skewness	66.5%	Mean frequency	74.9%
					Pulse factor	74.0%
					Centroid frequency	71.6%
					Frequency skewness	63.6%
Vibration signal	Kurtosis factor	71.8%	Margin factor	86.6%	Crest frequency	73.7%
	Margin factor	66.7%	Average frequency	63.9%	waveform factor	66.3%
	Root mean square	62.8%	Centroid frequency	−71.7%	Average frequency	63.3%
	Absolute mean	60.9%	Frequency standard deviation	−74.1%	Kurtosis factor	62.4%
					Pulse factor	61.0%
					Root mean square frequency	−61.5%
					Centroid frequency	−64.0%

. PCA was performed on the preferred features. The combination of principal components with a cumulative contribution rate of more than 99% was selected to construct a subset of preferred dimensionality reduction features. Finally, a 14-dimensional AE signal preferred dimensionality reduction feature subset AE_PCA and a 17-dimensional vibration signal preferred dimensionality reduction feature subset V_PCA were obtained.

From the AE and vibration signal of the whole process of grinding wheel wear, 30 groups of experimental signals were intercepted as model training and model test samples, as shown in

Table 4. Model training and test sample composition.

Sample	Amount	Range of Amrv (cm3)	Wear Stages of the Grinding Wheel	Amount
Training samples	24	0~16.53	Initial wear	8
		22.04~38.57	Stable wear	8
		44.08~60.01	Severely wear	8
Testing samples	6	0~16.53	Initial wear	2
		22.04~38.57	Stable wear	2
		44.08~60.01	Severely wear	2

. The optimal feature subset was used as the input of the BPNN to construct two sub-networks. The parameter settings are shown in

Table 5. BPNN parameter setting.

Signal	Input Feature Number	Number of Hidden Neurons	Output Feature Number	Convergence Error	Rate of Learning	Transfer Function	Learning Algorithm
AE	14	6	3	0.03	0.05	Sigmoid	Trainlm
Vibration	17	8	3	0.03	0.05	Sigmoid	Trainlm

. The output vector was T = [100; 010; 001], representing the three wear stages of grinding wheel initial wear T1 (100), stable wear T2 (010), and severe wear T3 (001).

$$l=\sqrt{(n+m)}+a$$

(17)

where n represents the number of neurons in the input layer, m represents the number of neurons in the output layer, and a is a constant between [1, 10].

The recognition results of the two BP sub-networks were used as the evidence body of D-S, and a decision-level recognition model was established to make decision fusion recognition. Firstly, the recognition framework of the fusion model was determined to be composed of three propositions: T1, T2, and T3. The output of the two BPNN sub-network models was normalized. Then, the basic probability distribution of the corresponding propositions was calculated. The original feature subset, the preferred feature subset, and the optimal feature subset were also used as the input of the BPNN-D-S model. The recognition results are shown in

Table 6. The recognition results of the grinding wheel wear recognition model based on different feature subsets.

Sample	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18
Feature subset	The original feature subset						The preferred feature subset						The optimal feature subset
Predicted Result	T1	T1	T2	*	*	T3	T1	T1	*	T2	*	T3	T1	T1	T2	T2	T3	T3
Actual Result	T1	T1	T2	T2	T3	T3	T1	T1	T2	T2	T3	T3	T1	T1	T2	T2	T3	T3
Reliability support (%)	87	85	91	46	43	85	91	93	50	92	51	96	95	96	95	96	94	93
Accuracy (%)	66.7						66.7						100

NOTES: * represents uncertainty.

.

However, the recognition results based on the original and the preferred feature subset as the input of the BPNN-D-S model were not ideal and did not meet the application requirements. The BPNN-D-S model accuracy based on the optimal feature subset is 100%, and the reliability support for any basic proposition is more than 93%. From the recognition results, the D-S decision model fused the uncertain components of the two BP sub-network models to eliminate the influence of uncertain components on the recognition results and improves the recognition accuracy. The hierarchical fusion of multi-sensor information ensures the feature set’s high correlation and low redundancy. At the same time, it eliminates the influence of the uncertain components of the BPNN model on the recognition. Therefore, the cloud-edge device collaborative intelligent monitoring system of grinding wheel wear state in high-speed cylindrical bearing ring grinding is constructed with the multi-sensor information hierarchical fusion as the core algorithm.

4. Application Development of Cloud-Edge Device Collaborative Intelligent Monitoring System

Based on the proposed cloud-edge device collaborative intelligent monitoring architecture and the grinding wheel wear states monitoring method based on multi-sensor information fusion, through the fusion application of the internet of things, edge computing, cloud computing, machine learning, cylindrical grinding knowledge, etc., a cloud–edge-end collaborative intelligent monitoring system for high-speed cylindrical grinding wheel wear state for bearing ring is here developed for use on a high-speed composite cylindrical grinder. Application verification was carried out, as shown in Figure 16.

4.1. Machin Tool Device End-High-Speed Cylindrical Grinding and Data Acquisition of the Bearing Ring

The main function of the machine tool device end is to carry out the high-speed cylindrical grinding of the bearing ring and to collect data, as shown in Figure 17. During the cylindrical grinding of the bearing ring, cylindrical cut-in grinding is used. There is no movement in the Z-axis direction. The bearing ring is driven to rotate along the C-axis through the special accessories of the outer circle, and the X-axis is the feed direction. The data acquisition mainly comes from two aspects. On the one hand, the equipment status data stored in the data system are obtained through the machine tool communication interface and directly transmitted to the cloud end. On the other hand, the grinding state data are collected by the electrical circuit of the machine tool and the external sensor. The multi-sensor real-time acquisition platform consists of sensors, amplifiers, acquisition cards, displays, and acquisition software.

The numerical control system of CNC8325 CNC high-speed compound cylindrical grinding machine is Siemens 840 D. Therefore, through the Profibus-DP communication bus function, network port, and other communication links, the communication protocol of data acquisition is constructed. The device status data are exchanged and shared between the PC and CNC through the ethernet (TCP/IP) interface, and the NC code is analyzed. The grinding process, time, and position parameters are read by calling some functions in the function library. The data acquisition host computer is connected to the grinder to extract the equipment state data. The data acquisition host computer software processes the device status data, which are transmitted through the local area network. The data acquisition terminal uses the client/server (C/S) architecture to communicate. It creates a specific port required for the server to listen, which is responsible for receiving the client connection request and generating the socket connected to the client. Finally, the device status data are sent to the cloud server as a message queue. Grinding state data, such as grinding chatter, grinding burns, grinding wheel wear, and other information about the grinding, are obtained through the electrical circuit of the equipment and the corresponding external sensors (vibration, power, AE, etc.). The AE signal can obtain high-frequency information about the grinding area. The mid-low frequency information in the vibration signal is rich. Although the power signal frequency is low, it can better explain the mechanism of grinding wheel wear. The information in the three signals is highly complementary to effectively improve the accuracy of system identification. At the same time, the interference sources of the signals are quite different, and the system’s robustness can be enhanced by information fusion. Finally, the programmable controller is used to collect multi-channel sensor data in combination with PLC programming, and the real-time signal data are transmitted to the edge database and edge software for further analysis.

4.2. Edge End Online Monitoring System of Grinding Wheel Wear Based on Labview and Matlab

As shown in Figure 18, an edge-end intelligent monitoring system was constructed. The multi-sensor synchronous acquisition software was developed by LabView to realize the multi-source heterogeneous data synchronous acquisition during grinding. At the same time, the grinding wheel wear state monitoring method based on multi-sensor information fusion is encapsulated into the edge-end software by MATLAB Script to realize the online monitoring of grinding wheel wear state in high-speed cylindrical grinding of bearing rings.

The raw data storage is related to the later data analysis and application. High-fidelity original data storage technology is needed to avoid losing important information. The technical data management (TDM) solution defined by NI company is used to store the signal in TDMS file format. TDMS file format consists of three levels: file, group, and channel. The data are divided into multiple groups according to the type and location of the sensor. The stored data of the TDMS file take up less memory in binary mode. It has fast storage and reading speed. It solves the problem of storing and reading real-time data during grinding. At the same time, the MYSQL database management system (DBMS) is built on the edge. MySQL can be personalized and designed according to project requirements to optimize the MySQL source program. It can meet the project’s needs and significantly reduce construction costs. Therefore, the monitoring system database system uses MySQL for data management.

Table 7. Tables corresponding relationship.

Relation	Example	The Principle of Building Tables
One for one	Machine tool model table	The foreign key is the primary key and unique
One-to-many	Machine tools and tool types	A foreign key in the table points to the main table’s primary key.
Many-to-many	Workpiece and material composition	The intermediate table has two foreign keys, which point to the primary key of the associated table.

represents the correspondence and table-building principles of the database table. Taking the machine tool information table in the basic information management module as an example, the table structure is shown in

Table 8. Machine tool information sheet.

Field Name	Field Type	Field Length	Whether the Primary Key	Remark
PRODUCT_ID	char	19, Not null	Yes	Primary key ID
MACHINE_TOOL_ID	char	19, Not null	No	Machine tool ID
MACHINE_TOOL_NAME	varchar	200, Not null	No	Equipment name
MACHINE_TOOL_CODE	varchar	36	No	Equipment number
MACHINE_TOOL_MODEL	varchar	36	No	Equipment type
MACHINE_TOOL_TYPE	varchar	36	No	Equipment type
MACHINE_TOOL_MAINER	varchar	36	No	Equipment Manager
DELIVERY_TIME	datetime	Not null	No	Factory time

.

4.3. Cloud-End Bearing Ring High-Speed Cylindrical Grinding Wheel Wear Remote Monitoring System

The cloud monitoring software system of grinding wheel wear state for high-speed grinding bearing rings adopts Java, HTML, CSS, JavaScript, and other programming languages to realize back-end service, data management, and front-end page development of processing data. The system uses Browser/Server (B/S) architecture, which can be realized only by a Web browser when accessing. It has robust interactivity. At the same time, the browser client, server, and DB database constitute a three-tier architecture.

The cloud monitoring system was developed by JAVA in IntelliJ IDEA. The technical architecture design is shown in Figure 19. The specific technology selection and development environment are shown in

Table 9. System technology selection and development environment.

Type/Content	Technology Selection	Type/Content	Technology Selection
Back-end development language	JAVA (JDK 1.7)	Persistence layer framework	Mybatis-Plus
Back-end core framework	SpringBoot	Template engine	Thymeleaf
Front-end development language	Html, CSS, JavaScript	Cache framework	Redis
Front-end core framework	LAYUI	Security framework	Apache Shiro
Database management system	MYSQL 5.7/SQL

. The cloud monitoring system grinding uses the MYSQL relational database as the data storage framework. It accesses data objects through SQL language to provide information sources for the presentation layer. The Mybatis data persistence layer establishes a persistent access connection to the database to verify the data and provide retrieval services. The business logic of processing data back-end service and data management is realized by interacting with the database. The SpringBoot business logic layer is based on Java to program the back-end business logic, which provides SpringBoot with call-related components to develop back-end applications quickly. The front end uses HTML, CSS, and JavaScript to design and develop the page. The Layui presentation layer is responsible for page design, data rendering, etc., and it is combined with JSP, AJAX, and other technologies to achieve dynamic interaction between front-end and back-end data.

The cloud monitoring software system of grinding wheel wear state during high-speed grinding of bearing rings includes the following functional modules: (1) The basic information query module, which covers the information query management and visualization of grinder, grinding wheel, workpiece model, grinding parameters, and machine meter data. (2) The physical data management module, which includes the original experimental data upload, storage, and management services. The module reconstructs the signal feature vector of the grinding state data (power, AE, and vibration signals) stored on the edge end in the Java background using a simple curve fitting algorithm to obtain a simplified sensor signal change curve, which can visually observe the sensor signal change. (3) The grinding wheel wear condition monitoring module, which includes sensor signal analysis and state recognition services. Based on the uploaded sensor signal features, the signal feature analysis is carried out, and the results are visualized. The feature screening is realized in the Java background to generate the filtered feature file and let the features correspond to the grinding wheel wear stage. According to the above grinding experimental data and the theory of grinding wheel wear state recognition model construction, combined with the online monitoring method of grinding wheel wear state based on multi-sensor information fusion proposed above, the online recognition model of grinding wheel grinding based on multi-sensor information fusion is established in the Java background through the MATLAB and the Java extension toolkit.

5. Conclusions

Aiming to address the problem of the grinding wheel being easy to wear during the high-speed grinding process of bearing rings, this paper has proposed a cloud-edge device collaborative intelligent online monitoring system framework. Combining the high-speed cylindrical grinding mechanism and data-driven recognition algorithm, the grinding wheel wear state was quantified, and the grinding wheel wear state’s high-precision and high-reliability online identification was realized. Under this framework, a cloud-edge device collaborative intelligent monitoring system was developed based on the online monitoring method of grinding wheel wear based on multi-sensor information fusion for the high-speed cylindrical grinding of the bearing ring to realize the real-time processing of real-time manufacturing data and the regulation of the manufacturing process of the grinder. The cloud-edge-device coordination interaction mechanism was used to realize the reasonable allocation of computing resources, which alleviates the transmission, calculation, and storage pressure of the cloud data processing center and realizes the rapid response of the online monitoring system. Our paper provides a reference for the factory to implement intelligent manufacturing mode.