A survey on data-driven process monitoring and diagnostic methods for variation reduction in multi-station assembly systems

2019 ◽  
Vol 39 (4) ◽  
pp. 727-739 ◽  
Author(s):  
Yinhua Liu ◽  
Rui Sun ◽  
Sun Jin
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
Fault Diagnosis ◽  
Process Monitoring ◽  
Information And Communications Technology ◽  
Diagnostic Methods ◽  
Data Driven ◽  
Assembly Systems ◽  
Content Type ◽  
Variation Reduction

PurposeDriven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.Design/methodology/approachThis paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.FindingsA classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.Originality/valueThis paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

scholarly journals Fault Diagnosis of DCV and Heating Systems Based on Causal Relation in Fuzzy Bayesian Belief Networks Using Relation Direction Probabilities

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6607
Author(s):  
Ali Behravan ◽  
Bahareh Kiamanesh ◽  
Roman Obermaisser
Keyword(s):  
Fuzzy Logic ◽  
Fault Diagnosis ◽  
Expert Knowledge ◽  
Causal Relation ◽  
Deep Understanding ◽  
Bayesian Belief Networks ◽  
Diagnostic Methods ◽  
Data Driven ◽  
Causal Relationships ◽  
Heating Systems

The state-of-the-art provides data-driven and knowledge-driven diagnostic methods. Each category has its strengths and shortcomings. The knowledge-driven methods rely mainly on expert knowledge and resemble the diagnostic thinking of domain experts with a high capacity in the reasoning of uncertainties, diagnostics of different fault severities, and understandability. However, these methods involve higher and more time-consuming effort; they require a deep understanding of the causal relationships between faults and symptoms; and there is still a lack of automatic approaches to improving the efficiency. The data-driven methods rely on similarities and patterns, and they are very sensitive to changes of patterns and have more accuracy than the knowledge-driven methods, but they require massive data for training, cannot inform about the reason behind the result, and represent black boxes with low understandability. The research problem is thus the combination of knowledge-driven and data-driven diagnosis in DCV and heating systems, to benefit from both categories. The diagnostic method presented in this paper involves less effort for experts without requiring deep understanding of the causal relationships between faults and symptoms compared to existing knowledge-driven methods, while offering high understandability and high accuracy. The fault diagnosis uses a data-driven classifier in combination with knowledge-driven inference with both fuzzy logic and a Bayesian Belief Network (BBN). In offline mode, for each fault class, a Relation-Direction Probability (RDP) table is computed and stored in a fault library. In online mode, we determine the similarities between the actual RDP and the offline precomputed RDPs. The combination of BBN and fuzzy logic in our introduced method analyzes the dependencies of the signals using Mutual Information (MI) theory. The results show the performance of the combined classifier is comparable to the data-driven method while maintaining the strengths of the knowledge-driven methods.

A New Gas Path Fault Diagnostic Method of Gas Turbine Based on Support Vector Machine

2015 ◽  
Vol 137 (10) ◽  
Author(s):  
Dengji Zhou ◽  
Huisheng Zhang ◽  
Shilie Weng
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Gas Turbine ◽  
Small Sample ◽  
Diagnostic Methods ◽  
Data Driven ◽  
Support Vector ◽  
Artificial Neural ◽  
Gas Path Fault ◽  
Turbine Model

As a crucial section of gas turbine maintenance decision-making process, to date, gas path fault diagnostic has gained a lot of attention. However, model-based diagnostic methods, like nonlinear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like expert system, need a knowledge database. Both are difficult to gain. Thus, data-driven approach for gas path diagnosis, like artificial neural network, is increasingly attractive. Support vector machine (SVM), a novel computational learning method, seems to be a good choice for data-driven gas path fault diagnosis of gas turbine. In this paper, SVM is employed to diagnose a deteriorated gas turbine. The effect of sample number, kernel function, and monitoring parameters on diagnostic accuracy are studied, respectively. Additionally, the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data and can be employed to gas path fault diagnosis of gas turbine. In addition, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnosis based on small sample.

A survey of the application of basic data-driven and model-based methods in process monitoring and fault diagnosis

2011 ◽  
Vol 44 (1) ◽  
pp. 12380-12388 ◽  
Author(s):  
S.X. Ding ◽  
P. Zhang ◽  
T. Jeinsch ◽  
E.L. Ding ◽  
P. Engel ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Process Monitoring ◽  
Basic Data ◽  
Data Driven ◽  
Model Based

Critical analysis of smart environment sensor data behavior pattern based on sequential data mining techniques

2015 ◽  
Vol 115 (6) ◽  
pp. 1151-1178 ◽  
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Chengliang Wang ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Pattern Mining ◽  
Sensor Data ◽  
Abnormal Behavior ◽  
Sequential Data ◽  
Research Issues ◽  
Content Type ◽  
Performance Optimizations ◽  
And Performance

Purpose – Behavioral pattern mining for intelligent system such as SmEs sensor data are vitally important in many applications and performance optimizations. Sensor pattern mining (SPM) is also dynamic and a hot research issue to pervasive and ubiquitous of smart technologies toward improving human life. However, in large-scale sensor data, exploring and mining pattern, which leads to detect the abnormal behavior is challenging. The paper aims to discuss these issues. Design/methodology/approach – Sensor data are complex and multivariate, for example, which data captured by the sensors, how it is precise, what properties are recorded or measured, are important research issues. Therefore, the method, the authors proposed Sequential Data Mining (SDM) approach to explore pattern behaviors toward detecting abnormal patterns for smart space fault diagnosis and performance optimization in the intelligent world. Sensor data types, modeling, descriptions and SPM techniques are discussed in depth using real sensor data sets. Findings – The outcome of the paper is measured as introducing a novel idea how SDM technique’s scale-up to sensor data pattern mining. In the paper, the approach and technicality of the sensor data pattern analyzed, and finally the pattern behaviors detected or segmented as normal and abnormal patterns. Originality/value – The paper is focussed on sensor data behavioral patterns for fault diagnosis and performance optimizations. It is other ways of knowledge extraction from the anomaly of sensor data (observation records), which is pertinent to adopt in many intelligent systems applications, including safety and security, efficiency, and other advantages as the consideration of the real-world problems.

Big Data-Driven Manufacturing—Process-Monitoring-for-Quality Philosophy

2017 ◽  
Vol 139 (10) ◽  
Author(s):  
Jeffrey A. Abell ◽  
Debejyo Chakraborty ◽  
Carlos A. Escobar ◽  
Kee H. Im ◽  
Diana M. Wegner ◽  
...  
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Big Data ◽  
Process Monitoring ◽  
Data Driven ◽  
Apparent Effect ◽  
Manufacturing Operations ◽  
Extended Range ◽  
Quality Movement ◽  
Efficient Manufacturing

Discussion of big data (BD) has been about data, software, and methods with an emphasis on retail and personalization of services and products. Big data also has impacted engineering and manufacturing and has resulted in better and more efficient manufacturing operations, improved quality, and more personalized products. A less apparent effect is that big data have changed problem solving: the problems we choose to solve, the strategy we seek, and the tools we employ. This paper illustrates this point by showing how the big data style of thinking enabled the development of a new quality assurance philosophy called process monitoring for quality (PMQ). PMQ is a blend of process monitoring and quality control (QC) that is founded on big data and big model (BDBM), which are catalysts for the next step in the evolution of the quality movement. Process monitoring (PM) for quality was used to evaluate the performance of the ultrasonically welded battery tabs in the new Chevrolet Volt, an extended range electric vehicle.

DATA MINING AS A MEANS OF QUALITY CONTROL IN PRODUCTION AND ORGANIZATION MANAGEMENT

2020 ◽  
Vol 1 (5) ◽  
pp. 130-138
Author(s):  
L. S. ZVYAGIN ◽  
Keyword(s):  
Data Mining ◽  
Quality Control ◽  
New Technologies ◽  
Data Interpretation ◽  
Organization Management

The article deals with data mining (IAD), which is widely used both in business and in various studies. IAD methods are used to create new ways to solve problems of forecasting, segmentation, data interpretation, etc. The problems to be solved by creating new technologies and methods of IAD are analyzed.

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