An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference

Sun, Yan-Ning; Qin, Wei; Zhuang, Zilong; Xu, Hongwei

doi:10.1007/s10845-021-01752-9

Cited by 55 publications

(12 citation statements)

References 46 publications

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“…RCA can then be performed based on features that are marked as the most relevant by the proposed approach. Among the frameworks that belong in the same ML and ANN family of methods, a scheme that consists of a moving window using kernel principal component analysis (KPCA) and an information geometric causal inference (IGCI) is reported in (Sun et al, 2021) and concerns the adaptive fault detection and RCA. Another method for fault root diagnosis is based on Recurrent Neural Network (RNN) and Granger Causality (GC), as proposed in (Shen et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Παπαγεωργίου

Theodosiou

Rapti

et al. 2022

Front. Manuf. Technol.

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The identification of defect causes plays a key role in smart manufacturing as it can reduce production risks, minimize the effects of unexpected downtimes, and optimize the production process. This paper implements a literature review protocol and reports the latest advances in Root Cause Analysis (RCA) toward Zero-Defect Manufacturing (ZDM). The most recent works are reported to demonstrate the use of machine learning methodologies for root cause analysis in the manufacturing domain. The popularity of these technologies is then summarized and presented in the form of visualizing graphs. This enables us to identify the most popular and prominent methods used in modern industry. Although artificial intelligence gains more and more attraction in smart manufacturing, machine learning methods for root cause analysis seem to be under-explored. The literature survey revealed that only limited reviews are available in the field of RCA towards zero-defect manufacturing using AI and machine learning; thus, it attempts to fill this gap. This work also presents a set of open challenges to determine future developments.

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Section: Resultsmentioning

confidence: 99%

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Παπαγεωργίου

Theodosiou

Rapti

et al. 2022

Front. Manuf. Technol.

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“…Li et al (2021) determined dimensional deviation with zero mean error and standard deviation of 0.02 mm using in situ optical monitoring of a material extrusion process. Sun et al (2021) employed adaptive fault detection and rootcause analysis using moving window KPCA and information geometric causal inference. They noted that this scheme had good performance in reducing the faulty false alarms and missed detection rates and locating fault root-cause.…”

Section: Process Monitoringmentioning

confidence: 99%

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

et al. 2022

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For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.

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“…On the other hand, this section adopts several typical feature fusion methods to fuse the channel feature data 17 Wireless Communications and Mobile Computing before alignment in Section 3 to verify the effectiveness of feature alignment and feature fusion methods based on multitype SAE. The methods mainly include kernel principal component analysis (KPCA), factor analysis (FA), linear discriminant analysis (LDA), and multidimensional scaling (MDS) [57][58][59][60]. The kernel function used in KPCA is the Gaussian kernel function.…”

Section: Performance Comparison Analysismentioning

confidence: 99%

Fault Diagnosis Algorithm of Gearbox Based on NAPSO-VMD Self-Adaptive Noise Reduction and Dual-Sensor Feature Fusion

Liu

Kang

Guo

et al. 2022

Wireless Communications and Mobile Computing

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Condition monitoring plays a very important role in equipment fault diagnosis technology. However, existing monitoring methods often collect equipment fault signals from a single dimension, resulting in a major lack of fault information. To improve the problem, we built a gearbox preset fault test bench and constructed a dual-sensor acquisition system to realize the multiple dimensions of vibration signal acquisition in the horizontal and vertical directions of the gearbox. At the same time, given the poor adaptability of most current signal preprocessing methods, the improved nonlinear adaptive inertial weight particle swarm optimization algorithm (NAPSO) and variational modal decomposition (VMD) are combined to optimize the key parameters in VMD with the maximum correlation kurtosis convolution (MCKD) as the fitness function. Further, after extracting fault features from the intrinsic mode functions (IMFS) decomposed by VMD, the single-layer sparse autoencoder network (SAE) and the double-layer stacked sparse autoencoder network (SSAE) with different structures are used to realize an effective fusion of multidimensional information and deep feature extraction. Finally, the hybrid fault diagnosis of gearboxes is realized by using the random forest algorithm (RF) as the classifier. The experimental results show that the accuracy of the method proposed in this paper can reach 96.0%, and the accuracy can be improved by 3.0% and 4.0%, respectively, when compared with a single horizontal or vertical sensor signal input.

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An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference

Cited by 55 publications

References 46 publications

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

A systematic review on machine learning methods for root cause analysis towards zero-defect manufacturing

Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control

Fault Diagnosis Algorithm of Gearbox Based on NAPSO-VMD Self-Adaptive Noise Reduction and Dual-Sensor Feature Fusion

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