Process monitoring and fault detection strategies: a review

Abstract

PurposeMonitoring of a process leading to the detection of faults and determination of the root causes are essential for the production of consistent good quality end products with improved yield. The history of process monitoring fault detection (PMFD) strategies can be traced back to 1930s. Thereafter various tools, techniques and approaches were developed along with their application in diversified fields. The purpose of this paper is to make a review to categorize, describe and compare the various PMFD strategies.Design/methodology/approachTaxonomy was developed to categorize PMFD strategies. The basis for the categorization was the type of techniques being employed for devising the PMFD strategies. Further, PMFD strategies were discussed in detail along with emphasis on the areas of applications. Comparative evaluations of the PMFD strategies based on some commonly identified issues were also carried out. A general framework common to all the PMFD has been presented. And lastly a discussion into future scope of research was carried out.FindingsThe techniques employed for PMFD are primarily of three types, namely data driven techniques such as statistical model based and artificial intelligent based techniques, priori knowledge based techniques, and hybrid models, with a huge dominance of the first type. The factors that should be considered in developing a PMFD strategy are ease in development, diagnostic ability, fault detection speed, robustness to noise, generalization capability, and handling of nonlinearity. The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.Research limitations/implicationsThe review documents the existing strategies for PMFD with an emphasis on finding out the nature of the strategies, data requirements, model building steps, applicability and scope for amalgamation. The review helps future researchers and practitioners to choose appropriate techniques for PMFD studies for a given situation. Further, future researchers will get a comprehensive but precise report on PMFD strategies available in the literature to date.Originality/valueThe review starts with identifying key indicators of PMFD for review and taxonomy was proposed. An analysis was conducted to identify the pattern of published articles on PMFD followed by evolution of PMFD strategies. Finally, a general framework is given for PMFD strategies for future researchers and practitioners.

Innovative Strategy for Increasing Competitiveness in Organizational Structures of Industrial Enterprises

“…The experience of economists which can be applied to solve this problem is useful [4,18,19,20,21,22].…”

State of the Art and Trends in the Monitoring, Detection and Diagnosis of Failures in Electric Induction Motors

“…According to Das et al [30], the application of strategies for the monitoring, detection and diagnosis of failures can be traced back to 1931 when Walter Shewhar used the control charts to determine if a process was under statistical control. At the end of the nineteenth century, the demand for industrialized products exceeded the supply, so companies focused on quantity rather than quality.…”

“…However, as indicated by [30], "The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently, and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy. Greater emphasis needs to be given to the development of PMFD strategies based on amalgamation of two or more techniques in order to deal with complex ever-evolving manufacturing processes."…”

“…In what refers strictly to the part corresponding to the Process to Monitor and Detect the Failures (PMFD) of electric motors, according to [30], the techniques employed depending on the diagnostic strategies are classified according to Table 7, while the advantages and disadvantages of each of the factors that should be considered when developing a PMFD are included in Table 8. However, as indicated by [30], "The review reveals that there is no single strategy that can address all aspects related to process monitoring and fault detection efficiently, and there is a need to mesh the different techniques from various PMFD strategies to devise a more efficient PMFD strategy.…”

Machine Learning for Data-driven Prognostics : Methods and Applications

“…Depending on the nature of the machine and the corresponding monitoring data to be processed, which is determined by the application domain and by the particular problem to be solved, these techniques have demonstrated a good accuracy when accomplishing fault detection tasks [109] [110] [111]. Failure modes analysis is usually performed to identify most probable causes of detected abnormal behaviors in order to avoid them in the future [112].…”