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19 pages, 18121 KiB

Open AccessArticle

Gap-MK-DCCA-Based Intelligent Fault Diagnosis for Nonlinear Dynamic Systems

by Junzhou Wu, Mei Zhang and Lingxiao Chen

Processes 2024, 12(2), 388; https://doi.org/10.3390/pr12020388 - 15 Feb 2024

Viewed by 563

Abstract

In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named [...] Read more.

In intelligent process monitoring and fault detection of the modern process industry, conventional methods mostly consider singular characteristics of systems. To tackle the problem of suboptimal incipient fault detection in nonlinear dynamic systems with non-Gaussian distributed data, this paper proposes a methodology named Gap-Mixed Kernel-Dynamic Canonical Correlation Analysis. Initially, the Gap metric is employed for data preprocessing, followed by fault detection utilizing the Mixed Kernel-Dynamic Canonical Correlation Analysis. Ultimately, fault identification is conducted through a contribution method based on the

T^{2}

statistic. Furthermore, a comparative analysis was conducted using Canonical Variate Analysis, Dynamic Canonical Correlation Analysis, and Mixed Kernel-Dynamic Canonical Correlation Analysis on the Tennessee Eastman process. Experimental results indicate varying degrees of improvements in the detection rate, false alarm rate, missed detection rate, and detection time compared to the comparative methods, demonstrating the industrial value and academic significance of the method. Full article

(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)

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16 pages, 6370 KiB

Open AccessArticle

Low-Power Very-Large-Scale Integration Implementation of Fault-Tolerant Parallel Real Fast Fourier Transform Architectures Using Error Correction Codes and Algorithm-Based Fault-Tolerant Techniques

by M. Kalpana Chowdary, Rajasekhar Turaka, Bayan Alabduallah, Mudassir Khan, J. Chinna Babu and Ajmeera Kiran

Processes 2023, 11(8), 2389; https://doi.org/10.3390/pr11082389 - 8 Aug 2023

Cited by 4 | Viewed by 1292

Abstract

As technology advances, electronic circuits are more vulnerable to errors. Soft errors are one among them that causes the degradation of a circuit’s reliability. In many applications, protecting critical modules is of main concern. One such module is Fast Fourier Transform (FFT). Real [...] Read more.

As technology advances, electronic circuits are more vulnerable to errors. Soft errors are one among them that causes the degradation of a circuit’s reliability. In many applications, protecting critical modules is of main concern. One such module is Fast Fourier Transform (FFT). Real FFT (RFFT) is a memory-based FFT architecture. RFFT architecture can be optimized by its processing element through employing several types of adder and multipliers and an optimized memory usage. It has been seen that various blocks operate simultaneously in many applications. For the protection of parallel FFTs using conventional Error Correction Codes (ECCs), algorithmic-based fault tolerance (ABFT) techniques like Parseval checks and its combination are seen. In this brief, the protection schemes are applied to the single RAM-based parallel RFFTs and dual RAM-based parallel RFFTs. This work is implemented on platforms such as field programmable gate arrays (FPGAs) using Verilog HDL and on application-specific integrated circuit (ASIC) using a cadence encounter digital IC implementation tool. The synthesis results, including LUTs, slices registers, LUT–Flip-Flop pairs, and the frequency of two types of protected parallel RFFTs, are analyzed, along with the existing FFTs. The two proposed architectures with the combined protection scheme Parity-SOS-ECC present an 88% and 33% reduction in area overhead when compared to the existing parallel RFFTs. The performance metrics like area, power, delay, and power delay product (PDP) in an ASIC of 45 nm and 90 nm technology are evaluated, and the proposed single RAM-based parallel RFFTs architecture presents a 62.93% and 57.56% improvement of PDP in 45 nm technology and a 67.20% and 60.31% improvement of PDP in 90 nm technology compared to the dual RAM-based parallel RFFTs and the existing architecture, respectively. Full article

(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)

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12 pages, 5620 KiB

Open AccessArticle

Intelligent Temperature Control of a Stretch Blow Molding Machine Using Deep Reinforcement Learning

by Ping-Cheng Hsieh

Processes 2023, 11(7), 1872; https://doi.org/10.3390/pr11071872 - 22 Jun 2023

Cited by 2 | Viewed by 1644

Abstract

Stretch blow molding serves as the primary technique employed in the production of polyethylene terephthalate (PET) bottles. Typically, a stretch blow molding machine consists of various components, including a preform infeed system, transfer system, heating system, molding system, bottle discharge system, etc. Of [...] Read more.

Stretch blow molding serves as the primary technique employed in the production of polyethylene terephthalate (PET) bottles. Typically, a stretch blow molding machine consists of various components, including a preform infeed system, transfer system, heating system, molding system, bottle discharge system, etc. Of particular significance is the temperature control within the heating system, which significantly influences the quality of PET bottles, especially when confronted with environmental temperature changes between morning and evening during certain seasons. The on-site operators of the stretch blow molding machine often need to adjust the infrared heating lamps in the heating system several times. The adjustment process heavily relies on the personnel’s experience, causing a production challenge for bottle manufacturers. Therefore, this paper takes the heating system of the stretch blow molding machine as the object and uses the deep reinforcement learning method to develop an intelligent approach for adjusting temperature control parameters. The proposed approach aims to address issues such as the interference of environmental temperature changes and the aging variation of infrared heating lamps. Experimental results demonstrate that the proposed approach achieves automatic adjustment of temperature control parameters during the heating process, effectively mitigating the influence of environmental temperature changes and ensuring stable control of preform surface temperature within ±2

℃

of the target temperature. Full article

(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)

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15 pages, 4642 KiB

Open AccessArticle

Reactor Temperature Control Based on Improved Fractional Order Self-Anti-Disturbance

by Xiaowei Tang, Bing Xu and Zichen Xu

Processes 2023, 11(4), 1125; https://doi.org/10.3390/pr11041125 - 5 Apr 2023

Cited by 3 | Viewed by 1729

Abstract

In the chemical industry, a reactor is an absolutely necessary container. The fact that its dynamic qualities are nonlinear and unknown, however, is what causes the temperature to deviate from the value that was specified. As a result, the typical PID control cannot [...] Read more.

In the chemical industry, a reactor is an absolutely necessary container. The fact that its dynamic qualities are nonlinear and unknown, however, is what causes the temperature to deviate from the value that was specified. As a result, the typical PID control cannot fulfill the prerequisites of the production process. A new nonlinear function is presented to replace the function that was previously used, and a temperature controller that is based on better fractional order active disturbance rejection is devised. On the basis of a new fractional order temperature detector (FOTD), a new fractional order equilibrium state observer (FOESO), and nonlinear function, an improved fractional order active disturbance rejection controller has been developed. A model of the reactor was created, and the dynamic properties of temperature control were investigated. By simulation and experimentation, it was demonstrated that the strategy has a number of benefits and is effective. In this approach, the information provided by the model is exploited to its maximum potential, and the temperature of the inlet cooling water is employed as the temperature control disturbance for feedforward compensation. Over the entirety of the process, this guarantees that the desired temperature will be preserved. When compared to FADRC, PID, and ADRC, the rising time is increased by 5 s, and the overshoot is raised by 25%. It has been established that the fraction-order active disturbance rejection controller has a quicker response speed, a higher capacity for anti-interference, and a quicker speed of stabilization. Full article

(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)

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20 pages, 1863 KiB

Open AccessArticle

An Improved Adaptive Dynamic Programming Algorithm Based on Fuzzy Extended State Observer for Dissolved Oxygen Concentration Control

by Xueliang Chen, Weimin Zhong, Xin Peng, Peihao Du and Zhongmei Li

Processes 2022, 10(12), 2618; https://doi.org/10.3390/pr10122618 - 7 Dec 2022

Cited by 1 | Viewed by 1664

Abstract

To solve the anti-disturbance control problem of dissolved oxygen concentration in the wastewater treatment plant (WWTP), an anti-disturbance control scheme based on reinforcement learning (RL) is proposed. An extended state observer (ESO) based on the Takagi–Sugeno (T-S) fuzzy model is first designed to [...] Read more.

To solve the anti-disturbance control problem of dissolved oxygen concentration in the wastewater treatment plant (WWTP), an anti-disturbance control scheme based on reinforcement learning (RL) is proposed. An extended state observer (ESO) based on the Takagi–Sugeno (T-S) fuzzy model is first designed to estimate the the system state and total disturbance. The anti-disturbance controller compensates for the total disturbance based on the output of the observer in real time, online searches the optimal control policy using a neural-network-based adaptive dynamic programming (ADP) controller. For reducing the computational complexity and avoiding local optimal solutions, the echo state network (ESN) is used to approximate the optimal control policy and optimal value function in the ADP controller. Further analysis demonstrates the observer estimation errors for system state and total disturbance are bounded, and the weights of ESNs in the ADP controller are convergent. Finally, the effectiveness of the proposed ESO-based ADP control scheme is evaluated on a benchmark simulation model of the WWTP. Full article

(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)

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16 pages, 4031 KiB

Open AccessArticle

Design of Soft-Sensing Model for Alumina Concentration Based on Improved Deep Belief Network

by Xiangquan Li, Bo Liu, Wei Qian, Guoyong Rao, Lijuan Chen and Jiarui Cui

Processes 2022, 10(12), 2537; https://doi.org/10.3390/pr10122537 - 29 Nov 2022

Cited by 3 | Viewed by 1253

Abstract

Alumina concentration is an important parameter in the production process of aluminum electrolysis. Due to the complex production environment in the industrial field and the complex physical and chemical reactions in the aluminum reduction cell, nowadays it is still unable to carry out [...] Read more.

Alumina concentration is an important parameter in the production process of aluminum electrolysis. Due to the complex production environment in the industrial field and the complex physical and chemical reactions in the aluminum reduction cell, nowadays it is still unable to carry out online measurement and real-time monitoring. For solving this problem, a soft-sensing model of alumina concentration based on a deep belief network (DBN) is proposed. However, the soft-sensing model may have some limitations for different cells and different periodic working conditions such as local anode effect, pole changing, and bus lifting in the same cell. The empirical mode decomposition (EMD) and particle swarm optimization (PSO) with the DBN are combined, and an EMD–PSO–DBN method that can denoize and optimize the model structure is proposed. The simulation results show that the improved soft-sensing model improves the accuracy and universality of prediction. Full article

(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)

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16 pages, 3723 KiB

Open AccessArticle

Research on Rolling-Element Bearing Composite Fault Diagnosis Methods Based on RLMD and SSA-CYCBD

by Jie Ma and Shitong Liang

Processes 2022, 10(11), 2208; https://doi.org/10.3390/pr10112208 - 27 Oct 2022

Cited by 2 | Viewed by 1291

Abstract

Aiming at the problem that it is difficult to separate and extract the composite fault features of rolling-element bearings, a composite fault diagnosis method combining robust local mean decomposition (RLMD), sparrow search algorithm (SSA), maximum second-order cyclostationarity blind deconvolution (CYCBD), is proposed. First, [...] Read more.

Aiming at the problem that it is difficult to separate and extract the composite fault features of rolling-element bearings, a composite fault diagnosis method combining robust local mean decomposition (RLMD), sparrow search algorithm (SSA), maximum second-order cyclostationarity blind deconvolution (CYCBD), is proposed. First, the RLMD is used to decompose the product function of the signal, and the two indicators, the excess and the correlation coefficient are then used as evaluation criteria to select the appropriate components for reconstruction. The reconstructed signal is then inputted into the SSA-optimized CYCBD algorithm, by specifying the objective function parameter which separates the faults and obtains multiple single fault signals with optimal noise reduction. Finally, envelope demodulation analysis is used for the multiple single fault signals, to obtain the characteristic frequencies of the corresponding faults, so as to complete the fault separation and feature extraction of composite faults. In order to verify the effectiveness of the method, the initial signals and the actual signals generated by the computer shall be used. The algorithm is verified using the XJTU-SY rolling-element bearing dataset, which shows the good performance of the method. Full article

(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)

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Review

Jump to: Research

28 pages, 984 KiB

Open AccessReview

A Survey on Programmable Logic Controller Vulnerabilities, Attacks, Detections, and Forensics

by Zibo Wang, Yaofang Zhang, Yilu Chen, Hongri Liu, Bailing Wang and Chonghua Wang

Processes 2023, 11(3), 918; https://doi.org/10.3390/pr11030918 - 17 Mar 2023

Cited by 8 | Viewed by 5482

Abstract

Programmable Logic Controllers (PLCs), as specialized task-oriented embedded field devices, play a vital role in current industrial control systems (ICSs), which are composed of critical infrastructure. In order to meet increasing demands on cost-effectiveness while improving production efficiency, commercial-off-the-shelf software and hardware, and [...] Read more.

Programmable Logic Controllers (PLCs), as specialized task-oriented embedded field devices, play a vital role in current industrial control systems (ICSs), which are composed of critical infrastructure. In order to meet increasing demands on cost-effectiveness while improving production efficiency, commercial-off-the-shelf software and hardware, and external networks such as the Internet, are integrated into the PLC-based control systems. However, it also provides opportunities for adversaries to launch malicious, targeted, and sophisticated cyberattacks. To that end, there is an urgent need to summarize ongoing work in PLC-based control systems on vulnerabilities, attacks, and security detection schemes for researchers and practitioners. Although surveys on similar topics exist, they are less involved in three key aspects, as follows: First and foremost, previous work focused more on system-level vulnerability analysis than PLC itself. Subsequently, it was not clear whether their work applied to the current systems or future ones, especially for security detection schemes. Finally, the prior surveys lacked a digital forensic research review of PLC-based control systems, which was significant for security analysis at different stages. As a result, we highlight vulnerability analysis at both a core component level and a system level, as well as attack models against availability, integrity, and confidentiality. Meanwhile, reviews of security detection schemes and digital forensic research for the current PLC-based systems are provided. Finally, we discuss future work for the next-generation systems. Full article

(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)

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23 pages, 3781 KiB

Open AccessFeature PaperReview

A Review of Real-Time Fault Diagnosis Methods for Industrial Smart Manufacturing

by Wenhao Yan, Jing Wang, Shan Lu, Meng Zhou and Xin Peng

Processes 2023, 11(2), 369; https://doi.org/10.3390/pr11020369 - 24 Jan 2023

Cited by 22 | Viewed by 5434

Abstract

In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud [...] Read more.

In the era of Industry 4.0, highly complex production equipment is becoming increasingly integrated and intelligent, posing new challenges for data-driven process monitoring and fault diagnosis. Technologies such as IIoT, CPS, and AI are seeing increasing use in modern industrial smart manufacturing. Cloud computing and big data storage greatly facilitate the processing and management of industrial information flow, which helps the development of real-time fault diagnosis (RTFD) technology. This paper provides a comprehensive review of the latest RTFD technologies in the field of industrial process monitoring and machine condition monitoring. The RTFD process is introduced in detail, starting with the data acquisition process. The current RTFD methods are divided into methods based on independent feature extraction, methods based on “end-to-end” neural networks, and methods based on qualitative knowledge reasoning from a new perspective. In addition, this paper discusses the challenges and potential trends of RTFD in future development to provide a reference for researchers focusing on this field. Full article

(This article belongs to the Special Issue Advanced Performance-Oriented Evaluation, Diagnosis and Fault-Tolerant Control Techniques for Industrial Processes)

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