Fault Detection Assessment using an extended FMEA and a Rule-based Expert System

Arevalo, Fernando; Tito, Cristhian; Diprasetya, Mochammad Rizky; Schwung, Andreas

doi:10.1109/indin41052.2019.8972299

Cited by 12 publications

(5 citation statements)

References 10 publications

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“…A methodology for digitalizing the eFMEA and generating rules for the expert system is proposed, allowing communication with equipment using OPC-UA protocol and handling multi-fault scenarios. A web application visualizes fault detection and diagnosis results, providing a clear description of the approach, implementation, benefits, limitations, and future work directions [24].…”

Section: ) Rule-based Expert Systemsmentioning

confidence: 99%

Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes

Kirdponpattara,

Sooraksa,

Boonjing

2024

IEEE Access

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The manufacturing of hard disk drives involves the intricate assembly of numerous components, making the testing process time-consuming and resource intensive. To optimize the manufacturing process and increase testing efficiency, the development of a rule-based expert system is proposed. This system leverages predictive models constructed from assembly process data to identify potentially defective hard drives before undergoing extensive testing. By preemptively identifying defects, this approach substantially reduces testing time and enhances tester capacity. Given the categorical and imbalanced nature of assembly data, Decision Trees are employed as the prediction model. Specifically, three Decision Tree algorithms are explored: ID3, C4.5, and CART. In addition, four feature selection techniques, namely Information

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Section: ) Rule-based Expert Systemsmentioning

confidence: 99%

Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes

Kirdponpattara,

Sooraksa,

Boonjing

2024

IEEE Access

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“…To this end, a knowledge extraction methodology using FMEA was presented in [26], where the authors proposed a strategy to formalize tacit knowledge, specifically machine faults, into a knowledge-based model in a systematic manner. This knowledge-based model contains rules that can be triggered using machine data.…”

Section: B Knowledge Modeling and Validationmentioning

confidence: 99%

“…Knowledge rule R can be used for process optimization or troubleshooting purposes. This article proposes an improved version of the extended FMEA from [26], which consists of a spreadsheet with four templates: settings, weight update, system and component. Moreover, this study formalizes the previous approach mathematically, allowing further improvements and modifications.…”

Section: ) Knowledge Extractionmentioning

confidence: 99%

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

Piolo

Tahasanul

et al. 2022

IEEE Access

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Operational knowledge is one of the most valuable assets in a company, as it provides a strategic advantage over competitors and ensures steady and optimal operation in machines. An (interactive) assessment system on the shop floor can optimize the process and reduce stopovers because it can provide constant valuable information regarding the machine condition to the operators. However, formalizing operational (tacit) knowledge to explicit knowledge is not an easy task. This transformation considers modeling expert knowledge, quantification of knowledge uncertainty, and validation of the acquired knowledge. This study proposes a novel approach for production assessment using a knowledge transfer framework and evidence theory to address the aforementioned challenges. The main contribution of this paper is a methodology for the formalization of tacit knowledge based on an extended failure mode and effect analysis for knowledge extraction, as well as the use of evidence theory for the uncertainty definition of knowledge. Moreover, this approach uses primitive recursive functions for knowledge modeling and proposes a validation strategy of the knowledge using machine data. These elements are integrated into an interactive recommendation system hosted on a backend that uses HoloLens as a visual interface. We demonstrate this approach using an industrial setup: a laboratory bulk good system. The results yield interesting insights, including the knowledge validation, uncertainty behavior of knowledge, and interactive troubleshooting for the machine operator.

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“…A knowledge-based FDD system integrating traditional data-driven FDD framework treats the knowledge pool (Figure 1) as an expert system [9,56]., where if-else rules are derived from maintainability rules. Following the existing rule-based system structures proposed in the related fields, such as [45,57,58], decisions for labeling various faults can be reached. The accuracy and performance of such FDD systems depend on the precision and reasonings of the rules.…”

Section: Knowledge-basedmentioning

confidence: 99%

Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management

Chew

Yan

2022

Journal of Sensors

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Data-driven fault detection and diagnosis (FDD) methods, referring to the newer generation of artificial intelligence (AI) empowered classification methods, such as data science analysis, big data, Internet of things (IoT), industry 4.0, etc., become increasingly important for facility management in the smart building design and smart city construction. While data-driven FDD methods nowadays outperform the majority of traditional FDD approaches, such as the physically based models and mathematically based models, in terms of both efficiency and accuracy, the interpretability of those methods does not grow significantly. Instead, according to the literature survey, the interpretability of the data-driven FDD methods becomes the main concern and creates barriers for those methods to be adopted in real-world industrial applications. In this study, we reviewed the existing data-driven FDD approaches for building mechanical & electrical engineering (M&E) services faults and discussed the interpretability of the modern data-driven FDD methods. Two data-driven FDD strategies integrating the expert reasoning of the faults were proposed. Lists of expert rules, knowledge of maintainability, international/local standards were concluded for various M&E services, including heating, ventilation air-conditioning (HVAC), plumbing, fire safety, electrical and elevator systems based on surveys of 110 buildings in Singapore. The surveyed results significantly enhance the interpretability of data-driven FDD methods for M&E services, potentially enhance the FDD performance in terms of accuracy and promote the data-driven FDD approaches to real-world facility management practices.

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Fault Detection Assessment using an extended FMEA and a Rule-based Expert System

Cited by 12 publications

References 10 publications

Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes

Building a Rule-Based Expert System to Enhance the Hard Disk Drive Manufacturing Processes

Production Assessment Using a Knowledge Transfer Framework and Evidence Theory

Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management

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