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Data-Driven Performance Monitoring and Management for Complex Manufacturing Processes
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Data-Driven Performance Monitoring and Management for Complex Manufacturing Processes

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Fault Diagnosis & Sensors".

Deadline for manuscript submissions: closed (25 September 2023) | Viewed by 8018

Special Issue Editors

Dr. Kai Zhang

E-Mail Website
Guest Editor
Department of Control Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: fault diagnosis; process monitoring; data-driven performance monitoring and management
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiwen Chen

E-Mail Website
Guest Editor
School of Automation, Central South University, Changsha, 410083, China
Interests: machine learning; data mining and analytic; PHM and fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kaixiang Peng

E-Mail Website
Guest Editor
Department of Control Science and Engineering, University of Science and Technology Beijing, Beijing 100083, China
Interests: big data; machine learning; advanced control; fault diagnosis with application to hot rolling mill
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to advanced sensing techniques, a massive amount of data are produced daily by manufacturing industrial activities. As a result, data-driven techniques aiming to make the best use of available data have received considerable attention in recent years, both in industry and academia. On the one hand, advanced data processing and information acquisition techniques have been developed such that large amounts of data in different forms are available for data analytics. On the other hand, with the help of machine learning techniques, monitoring and management methods can provide helpful and effective decision making for safe and optimal operation performance. Compared with traditional model-based techniques, data-driven monitoring and management methods not only negate the need for costly modeling processes but also obtain valuable information from available process data for real-time abnormality analysis and management. Such abnormalities, including different types of faults, can thus be timely detected and addressed. Nowadays, due to the ever-increasing complexity of manufacturing processes, there are many new challenging problems in this field, such as fault root-cause analysis for large-scale, plant-wide processes, performance-supervised monitoring with limited paired data, fault-tolerant control design in the distributed framework and so on.

This Special Issue aims to provide a platform for researchers to report their recent findings and emerging research developments in data-driven performance monitoring and management for manufacturing processes, especially in process monitoring, fault diagnosis, fault-tolerant control and machine-learning-relevant monitoring and management techniques, along with their applications.

Potential topics to be covered:

  1. Advanced sensing methods;
  2. Advanced process monitoring methods;
  3. Data-driven fault diagnosis and root-cause analysis;
  4. Data-driven fault hazard evaluation;
  5. Machine learning methods with applications in performance monitoring;
  6. Data-driven fault-tolerant control methods;
  7. Key-performance-indicator-supervised monitoring and management;
  8. Advanced deep learning and intelligent decision making;
  9. Big data analytics and implementation;
  10. Cloud-edge collaborative performance optimization.

Dr. Kai Zhang
Dr. Zhiwen Chen
Prof. Dr. Kaixiang Peng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (5 papers)

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Research

16 pages, 6773 KiB  
Article
Defect Identification Method for Transformer End Pad Falling Based on Acoustic Stability Feature Analysis
by Shuai Han, Bowen Wang, Sizhuo Liao, Fei Gao and Mo Chen
Sensors 2023, 23(6), 3258; https://doi.org/10.3390/s23063258 - 20 Mar 2023
Cited by 2 | Viewed by 1000
Abstract
A transformer’s acoustic signal contains rich information. The acoustic signal can be divided into a transient acoustic signal and a steady-state acoustic signal under different operating conditions. In this paper, the vibration mechanism is analyzed, and the acoustic feature is mined based on [...] Read more.
A transformer’s acoustic signal contains rich information. The acoustic signal can be divided into a transient acoustic signal and a steady-state acoustic signal under different operating conditions. In this paper, the vibration mechanism is analyzed, and the acoustic feature is mined based on the transformer end pad falling defect to realize defect identification. Firstly, a quality–spring–damping model is established to analyze the vibration modes and development patterns of the defect. Secondly, short-time Fourier transform is applied to the voiceprint signals, and the time–frequency spectrum is compressed and perceived using Mel filter banks. Thirdly, the time-series spectrum entropy feature extraction algorithm is introduced into the stability calculation, and the algorithm is verified by comparing it with simulated experimental samples. Finally, stability calculations are performed on the voiceprint signal data collected from 162 transformers operating in the field, and the stability distribution is statistically analyzed. The time-series spectrum entropy stability warning threshold is given, and the application value of the threshold is demonstrated by comparing it with actual fault cases. Full article
(This article belongs to the Special Issue Data-Driven Performance Monitoring and Management for Complex Manufacturing Processes)
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17 pages, 3543 KiB  
Article
Monitoring the Production Information of Conventional Machining Equipment Based on Edge Computing
by Yuguo Wang, Miaocong Shen, Xiaochun Zhu, Bin Xie, Kun Zheng and Jiaxiang Fei
Sensors 2023, 23(1), 402; https://doi.org/10.3390/s23010402 - 30 Dec 2022
Viewed by 1567
Abstract
A production status monitoring method based on edge computing is proposed for traditional machining offline equipment to address the deficiencies that traditional machining offline equipment have, which cannot automatically count the number of parts produced, obtain part processing time information, and discern anomalous [...] Read more.
A production status monitoring method based on edge computing is proposed for traditional machining offline equipment to address the deficiencies that traditional machining offline equipment have, which cannot automatically count the number of parts produced, obtain part processing time information, and discern anomalous operation status. Firstly, the total current signal of the collected equipment was filtered to extract the processing segment data. The processing segment data were then used to manually calibrate the feature vector of the equipment for specific parts and processes, and the feature vector was used as a reference to match with the real-time electric current data on the edge device to identify and obtain the processing start time, processing end time, and anomalous marks for each part. Finally, the information was uploaded to further obtain the part processing time, loading and unloading standby time, and the cause of the anomaly. To verify the reliability of the method, a prototype system was built, and extensive experiments were conducted on many different types of equipment in an auto parts manufacturer. The experimental results show that the proposed monitoring algorithm based on the calibration vector can stably and effectively identify the production information of each part on an independently developed edge device. Full article
(This article belongs to the Special Issue Data-Driven Performance Monitoring and Management for Complex Manufacturing Processes)
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18 pages, 3195 KiB  
Article
A Monitoring Method Based on FDALM and Its Application in the Sintering Process of Ternary Cathode Material
by Ning Chen, Fuhai Hu, Jiayao Chen, Kai Wang, Chunhua Yang and Weihua Gui
Sensors 2022, 22(19), 7203; https://doi.org/10.3390/s22197203 - 22 Sep 2022
Cited by 1 | Viewed by 1191
Abstract
In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states. In this situation, it is difficult for the traditional single-mode monitoring methods to accurately detect the process abnormalities. [...] Read more.
In industrial processes, the composition of raw material and the production environment are complex and changeable, which makes the production process have multiple steady states. In this situation, it is difficult for the traditional single-mode monitoring methods to accurately detect the process abnormalities. To this end, a multimode monitoring method based on the factor dynamic autoregressive hidden variable model (FDALM) for industrial processes is proposed in this paper. First, an improved affine propagation clustering algorithm to learn the model modal factors is adopted, and the FDALM is constructed by combining multiple high-order hidden state Markov chains through the factor modeling technology. Secondly, a fusion algorithm based on Bayesian filtering, smoothing, and expectation-maximization is adopted to identify model parameters. The Lagrange multiplier formula is additionally constructed to update the factor coefficients by using the factor constraints in the solving. Moreover, the online Bayesian inference is adopted to fuse the information of different factor modes and obtain the fault posterior probability, which can improve the overall monitoring effect of the model. Finally, the proposed method is applied in the sintering process of ternary cathode material. The results show that the fault detection rate and false alarm rate of this method are improved obviously compared with the traditional methods. Full article
(This article belongs to the Special Issue Data-Driven Performance Monitoring and Management for Complex Manufacturing Processes)
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21 pages, 6841 KiB  
Article
Fault Detection of Wind Turbine Gearboxes Based on IBOA-ERF
by Mingzhu Tang, Chenhuan Cao, Huawei Wu, Hongqiu Zhu, Jun Tang, Zhonghui Peng and Yifan Wang
Sensors 2022, 22(18), 6826; https://doi.org/10.3390/s22186826 - 09 Sep 2022
Cited by 6 | Viewed by 1615
Abstract
As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty [...] Read more.
As one of the key components of wind turbines, gearboxes are under complex alternating loads for a long time, and the safety and reliability of the whole machine are often affected by the failure of internal gears and bearings. Aiming at the difficulty of optimizing the parameters of wind turbine gearbox fault detection models based on extreme random forest, a fault detection model with extreme random forest optimized by the improved butterfly optimization algorithm (IBOA-ERF) is proposed. The algebraic sum of the false alarm rate and the missing alarm rate of the fault detection model is constructed as the fitness function, and the initial position and position update strategy of the individual are improved. A chaotic mapping strategy is introduced to replace the original population initialization method to enhance the randomness of the initial population distribution. An adaptive inertia weight factor is proposed, combined with the landmark operator of the pigeon swarm optimization algorithm to update the population position iteration equation to speed up the convergence speed and improve the diversity and robustness of the butterfly optimization algorithm. The dynamic switching method of local and global search stages is adopted to achieve dynamic balance between global exploration and local search, and to avoid falling into local optima. The ERF fault detection model is trained, and the improved butterfly optimization algorithm is used to obtain optimal parameters to achieve fast response of the proposed model with good robustness and generalization under high-dimensional data. The experimental results show that, compared with other optimization algorithms, the proposed fault detection method of wind turbine gearboxes has a lower false alarm rate and missing alarm rate. Full article
(This article belongs to the Special Issue Data-Driven Performance Monitoring and Management for Complex Manufacturing Processes)
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18 pages, 2094 KiB  
Article
Fault Detection for Wind Turbine Blade Bolts Based on GSG Combined with CS-LightGBM
by Mingzhu Tang, Caihua Meng, Huawei Wu, Hongqiu Zhu, Jiabiao Yi, Jun Tang and Yifan Wang
Sensors 2022, 22(18), 6763; https://doi.org/10.3390/s22186763 - 07 Sep 2022
Cited by 9 | Viewed by 1915
Abstract
Aiming at the problem of class imbalance in the wind turbine blade bolts operation-monitoring dataset, a fault detection method for wind turbine blade bolts based on Gaussian Mixture Model–Synthetic Minority Oversampling Technique–Gaussian Mixture Model (GSG) combined with Cost-Sensitive LightGBM (CS-LightGBM) was proposed. Since [...] Read more.
Aiming at the problem of class imbalance in the wind turbine blade bolts operation-monitoring dataset, a fault detection method for wind turbine blade bolts based on Gaussian Mixture Model–Synthetic Minority Oversampling Technique–Gaussian Mixture Model (GSG) combined with Cost-Sensitive LightGBM (CS-LightGBM) was proposed. Since it is difficult to obtain the fault samples of blade bolts, the GSG oversampling method was constructed to increase the fault samples in the blade bolt dataset. The method obtains the optimal number of clusters through the BIC criterion, and uses the GMM based on the optimal number of clusters to optimally cluster the fault samples in the blade bolt dataset. According to the density distribution of fault samples in inter-clusters, we synthesized new fault samples using SMOTE in an intra-cluster. This retains the distribution characteristics of the original fault class samples. Then, we used the GMM with the same initial cluster center to cluster the fault class samples that were added to new samples, and removed the synthetic fault class samples that were not clustered into the corresponding clusters. Finally, the synthetic data training set was used to train the CS-LightGBM fault detection model. Additionally, the hyperparameters of CS-LightGBM were optimized by the Bayesian optimization algorithm to obtain the optimal CS-LightGBM fault detection model. The experimental results show that compared with six models including SMOTE-LightGBM, CS-LightGBM, K-means-SMOTE-LightGBM, etc., the proposed fault detection model is superior to the other comparison methods in the false alarm rate, missing alarm rate and F1-score index. The method can well realize the fault detection of large wind turbine blade bolts. Full article
(This article belongs to the Special Issue Data-Driven Performance Monitoring and Management for Complex Manufacturing Processes)
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