A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches

Zhang, Kai; Hao, Haiyang; Chen, Zhiwen; Ding, Steven X.; Peng, Kaixiang

doi:10.1016/j.jprocont.2015.06.007

Cited by 169 publications

(48 citation statements)

References 43 publications

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“…In order to assess the monitoring performance, the false alarm rate (FAR) and fault detection rate (FDR) are used for performance evaluation . The FAR indicates the alarm rate under normal operating conditions and FDR indicates the detected effective alarm rate under fault conditions.…”

Section: Case Studymentioning

confidence: 99%

“…Since product quality is an important economic indicator in industrial processes, quality‐related fault detection, one of the monitoring tasks, has recently become a common research topic . If quality‐related faults occur in industrial processes that mainly focus on product quality, an effective traceability diagnosis and fault location would be performed.…”

Section: Introductionmentioning

confidence: 99%

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Distributed process monitoring framework based on decomposed modified partial least squares

et al. 2019

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With the growing complexity of industrial processes, the scale of production processes tends to be large. The significant amount of measurement data in large‐scale processes poses challenges in data collection, management, and storage. In order to perform effective process monitoring in large‐scale processes, the distributed process monitoring strategy is widely applied. Meanwhile, product quality is an important indicator for industrial production. Therefore, a novel quality‐based distributed process monitoring scheme is proposed. Firstly, the Girvan‐Newman (GN) algorithm in complex network divides process variables into multiple sub‐blocks. Secondly, greedy algorithm‐based high‐dimensional mutual information (HDMI) is used to extract quality‐related variables in each sub‐block, through which the irrelevant and redundant variables are eliminated. Thirdly, the decomposed modified partial least squares (DMPLS) approach is used to detect whether a fault is quality‐related or not in each sub‐block. Finally, the Bayesian inference strategy is adopted to combine the detection results of all sub‐blocks. The effectiveness of the distributed DMPLS approach is illustrated through a numerical simulation and the Tennessee Eastman (TE) process. The results show the superiority of our proposed monitoring scheme.

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Section: Case Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed process monitoring framework based on decomposed modified partial least squares

et al. 2019

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“…In engineering application, the detecting ability of the remote fault of stacker may meet the desired purpose using the proposed algorithm presented in [15,16]. Meanwhile, Kai Zhang and his coauthors have discussed and analyzed the monitoring method based on the multimode and multivariate statistics to monitor the running state of stacker crane in [17,18]. Experimental results show that the anomaly detection method is effective.…”

Section: Introductionmentioning

confidence: 99%

A New Cooperative Anomaly Detection Method for Stacker Running Track of Automated Storage and Retrieval System in Industrial Environment

Huang

Mengting

et al. 2018

Journal of Control Science and Engineering

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Considering the complexity and the criticality of the stacker equipment, in order to solve the problem that the stop accuracy of the stacker reduces or even fails to work due to abrasion of the running rail, this paper proposes a cooperative detection method based on Pulse Coupling Neural Network (PCNN) and wavelet transform theory to detect the abnormal points of the stacker running rail in industrial environment by analyzing the variation signals. First of all, considering the fact that the data is mixed up with noises because of the environment at the site and the possibility of the data acquisition equipment breaking down, a noise reduction method for the vibration signal data of stacker is constructed based on PCNN. Then, the basic theory of wavelet transform is introduced and then the rules of judging anomaly points on stackers' running tracks are discussed based on wavelet transform. In addition, a cooperative detection method based on PCNN and wavelet transform theory is carried out based on the spacetime distribution feature of the vibration of the stacker orbits in the industrial environment. Then the rationality of the proposed algorithm is verified by simulation through data provided by State Grid Measuring Center of China. This paper constructs a model of the abnormal point detection of the stackers in an industrial environment. The experimental simulation and example simulation show that the cooperative detection method based on PCNN and wavelet transform theory can effectively detect and locate the anomaly points of the stacker running tracks. The expansibility in engineering applications is promising. Lastly, some conclusions are discussed.

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“…It not only directly affects the safety of sea, land, and air transportation by making the outdoor surveillance system abnormal [1,2], but also hinders images feature extraction, which causes some monitoring systems based on feature extraction ineffective, such as production monitoring system [3,4].…”

Section: Introductionmentioning

confidence: 99%

An Improved Defogging Algorithm Based on Dark Color Theory Combined with Self-Adaptive Threshold Mechanism

Fang

2018

Journal of Control Science and Engineering

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Defogging algorithms based on dark channel prior have color shift in light color areas because of inaccurate estimation of transmittance. To resolve this problem, a novel improved image clearness method is proposed. Based on the dark channel prior, the essential causes of color shift are analyzed, with two important factors summarized. Then, transmission map is calculated by using 3 * 3 fixed region, and the restoration module based on self-adaptive threshold mechanism for transmission map is provided. Some experiments are carried out to determine parameters in restoration module to correct the transmission map. According to the corrected transmission map, a transmission restoration algorithm is constructed based on the self-adaptive threshold mechanism to improve the performance of the fog-free image. The experiment results show that this method can resolve the color shift in light color areas effectively and guarantee the overall framework of defogging method based on dark color theory unchanged.

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A comparison and evaluation of key performance indicator-based multivariate statistics process monitoring approaches

Cited by 169 publications

References 43 publications

Distributed process monitoring framework based on decomposed modified partial least squares

Distributed process monitoring framework based on decomposed modified partial least squares

A New Cooperative Anomaly Detection Method for Stacker Running Track of Automated Storage and Retrieval System in Industrial Environment

An Improved Defogging Algorithm Based on Dark Color Theory Combined with Self-Adaptive Threshold Mechanism

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