Fault Prediction Using Artificial Neural Network and Fuzzy Logic

Virk, Shafqat Mumtaz; Aslam, Muhammad; Martinez-Enriquez, A. M.

doi:10.1109/micai.2008.38

Cited by 20 publications

(7 citation statements)

References 6 publications

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“…In this paper, the residual life of ceramic capacitor is predicted using experimental as well as artificial intelligence techniques [7]. The experimental techniques include acceleration life testing technique and the artificial intelligence techniques include ANFIS, FL and ANN are employed to formulate the design of an intelligent model [8,9]. The flowchart of complete process is depicted in figure.4…”

Section: Methodsmentioning

confidence: 99%

DSSCC: Decision Support System for Ceramic Capacitor using Fuzzy Logic

Bhargava¹,

Gulati²,

Sharma³

2019

IJITEE

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As the technology advances, the reliability becomes the main constraint for the successful operation of the electronic product. To fully automate the system, the electronic devices become more and more complex. Reliability becomes a challenge with the regular demand of low cost and high-speed devices. Residual life estimation of passive devices such as resistor, capacitor etc. is of a great concern. Failure of one small component can lead to fully damage of whole system. In this paper, a practical approach i.e. accelerated life testing is deployed to calculate remaining useful life of the ceramic capacitor. An intelligent model is formulated using various artificial intelligence techniques. Artificial Neural Network (ANN), Fuzzy Inference System (FIS) as well as Adaptive Neuro Fuzzy Inference System (ANFIS) are deployed to predict the remaining useful lifetime of an electrolytic capacitor. An error analysis is conducted to estimate the most accurate intelligent technique. A fuzzy based decision support system is modelled, which provides an interactive GUI to users. The user can access the live health status of electrolytic capacitor at various input parameters. It will warn the user to replace or repair the upcoming fault in the component or device, before it actually degrades or shut downs the complete system. The comparative analysis of all the artificial intelligence techniques shows that Adaptive Neuro Fuzzy Inference System (ANFIS) has the highest accuracy i.e. 99.5%, as compare to Artificial Neural Network (ANN) and Fuzzy Inference System (FIS), where accuracy rate is 98.06% and 97.84% respectively. This prediction system is helpful to reduce the problem of electronic e-waste by enabling the user to reuse the component

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Section: Methodsmentioning

confidence: 99%

DSSCC: Decision Support System for Ceramic Capacitor using Fuzzy Logic

Bhargava¹,

Gulati²,

Sharma³

2019

IJITEE

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“…To determine the physical condition, traditional PM algorithms use sensor information like the machine's temperature, voltage, or current [18]. Typical PM approaches detect faults, i.e., low-frequency but high-impact events [3], or calculate an infrastructure's lifetime and metrics like the mean time to failure [19]. However, the definition of PM is ambiguous, but its main aim is to improve an infrastructure's operation using data [2].…”

Section: Theoretical Background 21 Predictive Maintenance For Facilitiesmentioning

confidence: 99%

A Text-Based Predictive Maintenance Approach for Facility Management Requests Utilizing Association Rule Mining and Large Language Models

Lowin

2024

MAKE

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Introduction: Due to the lack of labeled data, applying predictive maintenance algorithms for facility management is cumbersome. Most companies are unwilling to share data or do not have time for annotation. In addition, most available facility management data are text data. Thus, there is a need for an unsupervised predictive maintenance algorithm that is capable of handling textual data. Methodology: This paper proposes applying association rule mining on maintenance requests to identify upcoming needs in facility management. By coupling temporal association rule mining with the concept of semantic similarity derived from large language models, the proposed methodology can discover meaningful knowledge in the form of rules suitable for decision-making. Results: Relying on the large German language models works best for the presented case study. Introducing a temporal lift filter allows for reducing the created rules to the most important ones. Conclusions: Only a few maintenance requests are sufficient to mine association rules that show links between different infrastructural failures. Due to the unsupervised manner of the proposed algorithm, domain experts need to evaluate the relevance of the specific rules. Nevertheless, the algorithm enables companies to efficiently utilize their data stored in databases to create interpretable rules supporting decision-making.

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“…Herein, only moderate faults are developed and detected. Using a fuzzy logic-based model and artificial neural networks, The review of various vehicle fault prediction techniques [59]. To model a fault estimation service, different variables have been studies.…”

Section: A Diagnostic Of Electronic Componentsmentioning

confidence: 99%

Review of Health Prognostics and Condition Monitoring of Electronic Components

et al. 2020

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To meet the specifications of low cost, highly reliable electronic devices, fault diagnosis techniques play an essential role. It is vital to find flaws at an early stage in design, components, material, or manufacturing during the initial phase. This review paper attempts to summarize past development and recent advances in the areas about green manufacturing, maintenance, remaining useful life (RUL) prediction, and like. The current state of the art in reliability research for electronic components, mainly includes failure mechanisms, condition monitoring, and residual lifetime evaluation is explored. A critical analysis of reliability studies to identify their relative merits and usefulness of the outcome of these studies' visa -vis green manufacturing is presented. The wide array of statistical, empirical, and intelligent tools and techniques used in the literature are then identified and mapped. Finally, the findings are summarized, and the central research gap is highlighted. INDEX TERMS Reliability methods, condition monitoring, faults and failures, prognostics, diagnostics.

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Fault Prediction Using Artificial Neural Network and Fuzzy Logic

Cited by 20 publications

References 6 publications

DSSCC: Decision Support System for Ceramic Capacitor using Fuzzy Logic

DSSCC: Decision Support System for Ceramic Capacitor using Fuzzy Logic

A Text-Based Predictive Maintenance Approach for Facility Management Requests Utilizing Association Rule Mining and Large Language Models

Review of Health Prognostics and Condition Monitoring of Electronic Components

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