A Petri net approach to early failure detection and isolation for preventive maintenance

Yang, Shang-Kuo; Liu, T. S.

doi:10.1002/(sici)1099-1638(199809/10)14:5<319::aid-qre171>3.0.co;2-6

Cited by 37 publications

(3 citation statements)

References 22 publications

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“…The data-driven method includes hidden Markov model (HMM) based method [5][6][7][8], grey theory based method [9], dynamic Bayesian network (DBN) based method [10,11], and Wavelet neural network (WNN) based method [12]. The qualitative knowledge based method that includes expert system based model [13], Petri net based model [14] and so on can be adopted to establish the forecasting model of the hidden behavior.…”

Section: Q3mentioning

confidence: 99%

A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

Zhou

Zhang

et al. 2016

Applied Soft Computing

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Section: Q3mentioning

confidence: 99%

A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

Zhou

Zhang

et al. 2016

Applied Soft Computing

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“…Thus, it should entail a real-time diagnosis of the machinery status. Owing to its efficiency, CBM has received considerable attention in various industrial fields in recent years [5,6].…”

Section: Introductionmentioning

confidence: 99%

Machinery Fault Signal Detection with Deep One-Class Classification

Yoon,

2023

Applied Sciences

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Fault detection of machinery systems is a fundamental prerequisite to implementing condition-based maintenance, which is the most eminent manufacturing equipment system management strategy. To build the fault detection model, one-class classification algorithms have been used, which construct the decision boundary only using normal class. For more accurate one-class classification, signal data have been used recently because the signal data directly reflect the condition of the machinery system. To analyze the machinery condition effectively with the signal data, features of signals should be extracted, and then, the one-class classifier is constructed with the features. However, features separately extracted from one-class classification might not be optimized for the fault detection tasks, and thus, it leads to unsatisfactory performance. To address this problem, deep one-class classification methods can be used because the neural network structures can generate the features specialized to fault detection tasks through the end-to-end learning manner. In this study, we conducted a comprehensive experimental study with various fault signal datasets. The experimental results demonstrated that the deep support vector data description model, which is one of the most prominent deep one-class classification methods, outperforms its competitors and traditional methods.

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“…If some qualitative knowledge about a system is known, the qualitative knowledge-based method, such as expert system-based method [1] and Petri net-based method [30], can be used to infer the hidden behavior of the system. If the hybrid information is available, a number of theoretical methods have been employed to use the hybrid information for predicting the hidden behavior, such as hidden Markov model (HMM) [2], [8], [12], [15], [16], [32]- [34], [37], dynamic Bayesian network (DBN) [9], [13], [17], [19], [22], domain knowledge and statistical model-based method [10], qualitative knowledge and linear partial least square-based method [11], and fuzzy logic and artificial neural network (FL-ANN)-based method [18], [25].…”

mentioning

confidence: 99%

Hidden Behavior Prediction of Complex Systems Based on Hybrid Information

Zhou

Zhang

et al. 2013

IEEE Trans. Cybern.

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It is important to predict both observable and hidden behaviors in complex engineering systems. However, compared with observable behavior, it is often difficult to establish a forecasting model for hidden behavior. The existing methods for predicting the hidden behavior cannot effectively and simultaneously use the hybrid information with uncertainties that include qualitative knowledge and quantitative data. Although belief rule base (BRB) has been employed to predict the observable behavior using the hybrid information with uncertainties, it is still not applicable to predict the hidden behavior directly. As such, in this paper, a new BRB-based model is proposed to predict the hidden behavior. In the proposed BRB-based model, the initial values of parameters are usually given by experts, thus some of them may not be accurate, which can lead to inaccurate prediction results. In order to solve the problem, a parameter estimation algorithm for training the parameters of the forecasting model is further proposed on the basis of maximum likelihood algorithm. Using the hybrid information with uncertainties, the proposed model can combine together with the parameter estimation algorithm and improve the forecasting precision in an integrated and effective manner. A case study is conducted to demonstrate the capability and potential applications of the proposed forecasting model with the parameter estimation algorithm.

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A Petri net approach to early failure detection and isolation for preventive maintenance

Cited by 37 publications

References 22 publications

A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

A method for predicting the network security situation based on hidden BRB model and revised CMA-ES algorithm

Machinery Fault Signal Detection with Deep One-Class Classification

Hidden Behavior Prediction of Complex Systems Based on Hybrid Information

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