Probabilistic Reasoning for Improving the Predictive Maintenance of Vital Electrical Machine: Case Study

Lakehal, Abdelaziz; Ramdane, Ahmed; Tachi, Fouad

doi:10.25073/jaec.201821.74

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…Refs. [70][71][72][73][74][75][76][77][78][79][80][81][82][83][84] have shown various works applying statistical and probabilistic modeling approaches such as hidden Markov models (HMMs), Bayesian networks (BNs), Gaussian mixture models (GMMs), extreme gradient boosting (XGBoost), Density-based spatial clustering (DBSC), principal component analysis (PCA), and K-means to PdM tasks. Moreover, they introduced different DNN models, such as LSTM and autoencoders, for the tasks.…”

Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

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Section: State-of-the-art Techniques For Predictive Maintenancementioning

confidence: 99%

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Ucar,

Karakose,

Kırımça

2024

Applied Sciences

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“…In [15], the authors develop a Bayesian network for fault assessment of an electrical motor. Their proposed model is able to calculate through inference the probability of rotor fault of an induction motor and de ine the weakest branch in the structure of the Bayesian network that leads to failure by determining the probabilities of intermediate events.…”

Section: Related Workmentioning

confidence: 99%

Agnostic learning for packing machine stoppage prediction in smart factories

Gabriel¹,

Katsidimas²,

Nikoletseas³

et al. 2022

ITU J-FET

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The cyber-physical convergence is opening up new business opportunities for industrial operators. The need for deep integration of the cyber and the physical worlds establishes a rich business agenda towards consolidating new system and network engineering approaches. This revolution would not be possible without the rich and heterogeneous sources of data, as well as the ability of their intelligent exploitation, mainly due to the fact that data will serve as a fundamental resource to promote Industry 4.0. One of the most fruitful research and practice areas emerging from this data-rich, cyber-physical, smart factory environment is the data-driven process monitoring field, which applies machine learning methodologies to enable predictive maintenance applications. In this paper, we examine popular time series forecasting techniques as well as supervised machine learning algorithms in the applied context of Industry 4.0, by transforming and preprocessing the historical industrial dataset of a packing machine's operational state recordings (real data coming from the production line of a manufacturing plant from the food and beverage domain). In our methodology, we use only a single signal concerning the machine's operational status to make our predictions, without considering other operational variables or fault and warning signals, hence its characterization as "agnostic". In this respect, the results demonstrate that the adopted methods achieve a quite promising performance on three targeted use cases.

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“… Class A: Normal starting torque, high starting current and low operating slip,  Class B: Normal starting torque, low starting current and low operating slip,  Class C: High starting torque and low starting current,  Class D: High starting torque, low starting current and high operating slip. The work presented in this paper is the continuation of the machine separately [30][31][32]. In this contribution, real practical industrial application for indicating all stator and rotor faults together with their causes at the same time is presented.…”

Section: Validation Of the Model By Case Studymentioning

confidence: 99%

Bulletin of the Polish Academy of Sciences: Technical Sciences

Lakehal¹

2020

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In an effort to achieve an optimal availability time of induction motors via fault probabilities reduction and improved prediction or diagnostic tools responsiveness, a conditional probabilistic approach was used. So, a Bayesian network (BN) has been developed in this paper. The objective will be to prioritize predictive and corrective maintenance actions based on the definition of the most probable fault elements and to see how they serve as a foundation for the decision framework. We have explored the causes of faults for an induction motor. The influence of different power ranges and the criticality of the electric induction motor are also discussed. With regard to the problem of induction motor faults monitoring and diagnostics, each technique developed in the literature concerns one or two faults. The model developed, through its unique structure, is valid for all faults and all situations. Application of the proposed approach to some machines shows promising results on the practical side. The model developed uses factual information (causes and effects) that is easy to identify, since it is best known to the operator. After that comes an investigation into the causal links and the definition of the a priori probabilities. The presented application of Bayesian networks is the first of its kind to predict faults of induction motors. Following the results of the inference obtained, prioritizations of the actions can be carried out.

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Probabilistic Reasoning for Improving the Predictive Maintenance of Vital Electrical Machine: Case Study

Cited by 3 publications

References 2 publications

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Agnostic learning for packing machine stoppage prediction in smart factories

Bulletin of the Polish Academy of Sciences: Technical Sciences

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