1. Introduction
Industrial equipment is usually complicated and features multiple failure modes. As a large-scale rotate machine, once failure occurs, a long-time repairing, even shutdown, is required, resulting in a significant economic loss. On the other hand, industrial field characterized harsh operational environment, equipment would experience a high failure rate, therefore, accurate condition monitoring for industrial equipment is necessary, and plays a pivotal role in condition-based maintenance, which would be more beneficial than corrective and preventive maintenance [
1,
2].
Tavner [
3] concluded the general condition monitoring for industrial equipment, including temperature, chemical emission, vibration, electrical current, and electrical discharging. Furthermore, the paper illustrated the statistics distribution of failure modes in industrial equipment in the past decades, according to the research, rotation related failure occupies nearly a half (44%), higher than stator related (35%) and bearing related failure (21%), and according to previous work, unexpected vibration often occurs before serious failure events [
4,
5].
As a typical condition monitoring parameter, vibration is chosen for example and investigated in our paper. The aim of condition monitoring is inferring the condition of equipment rapidly, and giving a clear indication for incipient failure modes using minimum measurements. In industrial fields, vibrations are directly measured, for example, vibrations data are recorded in supervisory information system (SIS) in thermal power plants. In normal operating conditions, the vibrations of different directions are controlled in a certain range. The unexpected increase of vibrations corresponds to over-load, poor lubrication, bearing loosening, rotor unbalance, or shaft bending [
6]. Therefore, effective condition monitoring could evaluate equipment effectively, and the failures would be detected in early stage.
The state-of-the-art for rotate equipment vibration condition monitoring is concluded into directly signal monitoring and model-based monitoring. In the aspect signal analysis, time domain analysis and frequency domain analysis are effective methods for condition monitoring of rotate component vibration [
6,
7,
8,
9]. However, for large rotating equipment, such as blower fan, there are some difficult to realize signal condition monitoring: (1) blower fan working at variable power load and frequency. Working condition of rotating equipment changes frequently, which leads to frequency band width of vibration changed, resulting in poor accuracy of failure location. (2) The industrial environment is complicated, the vibration sources of each component are multiple and seriously coupling, therefore, the vibration analysis of components is difficult to reflect characteristic of blower fan. With the development and application of information technology in industrial fields, operating data, including vibration data, of equipment is recorded automatically in detail, which makes it possible to achieve condition monitoring using data-driven analysis methods. Expert system is an effective implementation for data-driven artificial intelligent method, an expert system for industrial machine vibration analysis is designed for condition monitoring, and the knowledge set was commonly constructed using operating data and laboratory and industry validation [
10,
11]. Neural network (NN) is an available method for nonlinear vibration; the model between recorded parameters and vibration state is established; the knowledge in historical data is acquired in the course of model training. The derivative neural network, such as recurrent neural network and convolutional neural network are also applied in vibration condition monitoring [
12,
13,
14,
15,
16]. However, the weaknesses of NN are also obvious, the training process is indispensable and it is always time-consuming. Furthermore, the adequate actual failure events data are difficult to obtain in industrial fields, the labeled data are the key component for improving the vibration monitoring accuracy in model training.
A concise and effective method is establishing an exact nonlinear normal state envelope space using normal vibration data, normal operating condition is completely included in the normal state envelope space, and the distance between current condition and boundary of success envelop space is used to quantify the condition monitoring. Cempel [
17] proposed vibration condition monitoring method applying symptom observation matrix; discrete observation symptom vectors were obtained by analyzing historical data, and the vectors create symptom observation matrix, using next the singular value decomposition for the given symptom observation matrix, fault symptoms could be extracted. MSET(multi-variables state estimate technique) was first proposed by Gross [
18] and applied in sensor and gearbox monitoring [
19,
20]. MSET is proved as an alternative method for constructing normal state envelope space. In general, our paper focused on proposing an improved and universal condition monitoring method and judging criteria, although the vibration condition monitoring for a specific equipment is applied and discussed in our paper, obviously, the condition monitoring for the other measurement parameters for different industrial equipment, such as temperature, current, etc., could be realized by reusing the proposed method.
In this paper, the normal state envelope space construction method for industrial equipment condition monitoring is proposed based on MSET, and the variables selecting for normal state envelope space are discussed. For a further feasibility discussion, a specific application in blower fan vibration condition monitoring is proposed. The weighted-similarity between the observation vibration vector and estimated vibration vector is investigated. While the potential failure occurred, there will be distinguished difference in similarity distribution, which allows incipient failure to be detected at early stage.
The rest of paper is organized as follows: In
Section 2, introduction of a typical industrial blower fan, which is applied for discussing in our research, is provided, and the parameters for blower fan are exhibited. In
Section 3, the structure for vibration condition monitoring model using MSET is discussed. In
Section 4, the vibration condition monitoring simulation is presented, including variables selection, model construction, similarity calculation, and alarm threshold determination. In
Section 5, the actual industrial data are applied for validation, and actual failure data for blower fan have been used for revealing the effectiveness of proposed method. Finally,
Section 6 concludes the work.
2. Introduction of Research Object
A typical industrial blower fan is discussed in our research. It is manufactured by TLT Cooperation, equipped in a thermal power plant in northern China, used for conveying air into furnace of boiler for combustion. The blades of blower fan are integrated with driving motor by shaft, and the shaft is sustained by bearings. While the motor is running, the blades rotate and air is injected into the furnace of the boiler. The structure for blower fan is shown in
Figure 1, and the actual layout and location of measuring points for investigated blower fan are also exhibited.
The Supervisory Information System (SIS) for the power plant records the parameters for blower fan in a 20-s sampling solution. In each sample, related parameters include: time stamp, current air volume, load of unit, vibration of bearings, temperature of bearings, current of driving motor, operation frequency of driving motor, and power of driving motor. The parameters are shown in
Table 1. Particularly, the temperature values, including temperature of bearings and end-side bearings, are measured using platinum resistance temperature detector. Before variables selection for condition monitoring, the 2 out of 3 actions are accomplished, the absolute errors between each of the two temperature values are compared, and the average of temperatures whose absolute error is smallest is regard as the actual value of measurement, which would improve the reliability of the measurement.
Except the parameters, the actual vibration alarms were also recorded in Supervisory Information System; while the vibration values exceed the thresholds setting ahead, the time stamp is recorded. The 7-day normal condition data are available for modeling. Meanwhile, actual failure alarms are acquired in our investigated blower fan, which is used for incipient failure detecting validation in our papers.
3. Multi-Variable State Estimate Technique (MSET)
By applying MSET, considerable historical data for industrial equipment are used for constructing the normal state envelope space, which is noted as memory matrix
D. While the industrial equipment operating, the measurement parameters are observed real-time, and the related
n parameters construct a vector noted as
Xobs. In the time sample
j, the observation vector is written as,
It should be pointed out that for different equipment condition monitoring, only the parameters and thresholds need to be determined if reusing the proposed method, and the complete preprocess of the case study in our paper is posed in
Section 4.
D is an
m ×
n dimension matrix, which means
m normal samples are strictly selected and contained in
D at last. Comprehensive operating conditions, including high load, low load, change load, and other operating conditions should be contained. Memory matrix
D could be written as,
The vectors included in the memory matrix D construct a nonlinear normal state envelope space. After the memory matrix D constructed, dynamic state estimation could be accomplished by applying it.
The construction of estimation vector for the same time sample
Xest is constructed by the vectors in memory matrix
D, the different of
Xest and
Xobs is applied for condition monitoring.
Xest could be written as,
The weight matrix
W stands for similarity between observation vector and components in matrix
D. The optimized weight matrix W could be obtained by minimizing the error between
Xest and
Xobs, which is,
According to the least square algorithm, for minimizing
let,
where,
Dij is the element for memory matrix in
i line
j column.
Solving the partial derivative Equation for
wi in
S(
w), which is,
The Equation (7) could be expressed as matrix form as,
The weight matrix could be solved, that is,
In particular, if components in memory matrix
D are linear correlative, the matrix
would be irreversible, which leads the (9) unsolvable. In order to solve the problem properly, there are many alternative operator; Argonne laboratory has also developed a unique algorithm for nonlinear calculation [
15]. A nonlinear operator
is introduced, and the Equation (9) could be written as,
Concise nonlinear operator that calculates the Euclidean distance arithmetic is chosen in our paper, that is,
The operator we employed is easy to understand physically, and the similarity of two vectors is represented by spatial distance. If the observation vector is similar with the vectors in memory matrix
D, the corresponding spatial distances would be small, and while there is a distinguishable different between observation vector and memory matrix, the distance would become large. Finally, the estimation vector could be written as,
The memory matrix D established a normal nonlinear normal state envelope space, while the input observation vector Xobs is located in the normal space and similar with a vector in D, the corresponding vector Xest obtained by Equation (12) is rather accurate. However, if an incipient failure occurs, the characteristic of Xobs would change, and the observation vector Xobs would exceed the boundary of envelope space, as a result, Xobs could not be represented by using the vectors included in memory matrix D, and the current observation vector Xobs and the estimate vector Xest would be very different. In conclusion, the incipient failure could be detected by analyzing similarity between Xobs and Xest.
6. Conclusions
An industrial equipment condition monitoring method based on MEST is proposed in this paper, and a novel method for construction of the memory matrix is put forward. The improved similarity that combines relative information in other variables is applied in monitoring. For different equipment and monitoring parameters, the method could be applied. The actual operational data for a blower fan in thermal power plant is used for validation, and the condition estimation performances are discussed. The actual data for failure event are used for validating the effectiveness of incipient failure detection employing proposed condition monitoring and early alarm method, the performance is compared with traditional exceed threshold method.
The method has advantages such as, it is concise and easy to understand conceptually, which should be attractive to industrial engineers. Irrespective of the steady or dynamic work condition, the proposed memory matrix construction method improves the accuracy of condition monitoring. The proposed method using residual and similarity enhances the alarm timeliness, and proposed method could identify the incipient failure ahead.
Although the proposed method has been employed in a specific blower fan, the model can be further explored with suitable parameters selection and threshold determination. The method should be further proved for different industrial equipment, and further research is planned for taking the method into account in other industrial equipment.