Essential Motor Health Monitoring: Making Informed Decisions About Motor Maintenance Before a Failure Occurs

Loiselle, Richard; Xu, Zhanwei; Voloh, Ilia

doi:10.1109/mias.2017.2740465

Cited by 11 publications

(8 citation statements)

References 2 publications

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“…e second class contains failures such as the bearing expansion by heat, the excessive wear of bearing, the excessive vibration of motor in operation, the abnormal noise, the rotor stuck or stopped rotating, and the motor sweeping. According to the principle of motor failures [23,[37][38][39][40][41] and the fault simulation model in Figure 2, fault fuzzy production rules of motors are obtained as follows, where events corresponding to the propositions in the rules are shown in Table 1.…”

Section: Fault Fuzzy Production Rules Formentioning

confidence: 99%

“…Historical statistics and expertise [37] show that most faults of three-phase induction motors are related to bearings, windings, and stators. Consequently, five relevant typical cases have been considered, which are "insulation winding burned down," "excessive wear of bearing," "motor overheating," "motor overheating and abnormal rotation or the rotor is stuck," and "phase-absent operation, abnormal rotation, or the rotor is stuck and excessive wear of bearing," respectively.…”

Section: Comparisonsmentioning

confidence: 99%

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A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems

et al. 2021

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The fault prediction and abductive fault diagnosis of three-phase induction motors are of great importance for improving their working safety, reliability, and economy; however, it is difficult to succeed in solving these issues. This paper proposes a fault analysis method of motors based on modified fuzzy reasoning spiking neural P systems with real numbers (rMFRSNPSs) for fault prediction and abductive fault diagnosis. To achieve this goal, fault fuzzy production rules of three-phase induction motors are first proposed. Then, the rMFRSNPS is presented to model the rules, which provides an intuitive way for modelling the motors. Moreover, to realize the parallel data computing and information reasoning in the fault prediction and diagnosis process, three reasoning algorithms for the rMFRSNPS are proposed: the pulse value reasoning algorithm, the forward fault prediction reasoning algorithm, and the backward abductive fault diagnosis reasoning algorithm. Finally, some case studies are given, in order to verify the feasibility and effectiveness of the proposed method.

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Section: Fault Fuzzy Production Rules Formentioning

confidence: 99%

Section: Comparisonsmentioning

confidence: 99%

A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems

et al. 2021

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“…Indeed, the detection and identification of electromechanical system conditions present new challenges in the modern industry framework. Such requirement represents a serious issue, thereby, new fusion information strategies, based on the combination of different physical magnitudes, and the exploitation of databases by means of reliable data-driven methodologies, represent the most promising approaches to describe unequivocal patterns related to the different conditions [8][9]. Certainly, the detection of operating novelties, novelty detection, is considered as a required competency for the next generation of condition-based monitoring (CBM) schemes.…”

Section: Introductionmentioning

confidence: 99%

Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults

Saucedo-Dorantes

Delgado-Prieto

Osornio-Ríos

et al. 2020

IEEE Trans. Ind. Inf.

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The detection of uncharacterized events during electromechanical systems operation represents one of the most critical data challenges dealing with condition-based monitoring under the Industry 4.0 framework. Thus, the detection of novelty conditions and the learning of new patterns are considered as mandatory competencies in modern industrial applications. In this regard, this study proposes a novel multi-fault detection and identification scheme, based on machine learning, information data-fusion, novelty-detection and incremental learning. First, statistical time-domain features estimated from multiple physical magnitudes acquired from the electrical motor under inspection are fused under a feature-fusion level scheme. Second, a selforganizing map structure is proposed to construct a data-based model of the available conditions of operation. Third, the incremental learning of the condition-based monitoring scheme is performed adding self-organizing structures and optimizing their projections through a linear discriminant analysis. The performance of the proposed scheme is validated under a complete set of experimental scenarios and results compared with a classical approach.

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“…asset loss and blackout of nuclear power equipment), and it may even generate a serious social impact. Therefore, it is of great theoretical and practical significance to conduct effective state evaluation, health analysis as well as in-depth fault diagnosis study on the nuclear power motor, guide the operation maintenance and state maintenance of motor, and prevent as well as reduce the occurrence probability of fault (Loiselle, Xu, & Voloh, 2018).…”

Section: Introductionmentioning

confidence: 99%

A low-carbon evaluation method for manufacturing products based on fuzzy mathematics

Dong

2020

Systems Science & Control Engineering

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According to the functional structure and design structure of the manufacturing products, a lowcarbon evaluation index system is established for product maintenance and support system, where the particularity of the working environment of manufacturing products, the maintenance ability and the support tasks of equipment manufacturing products are all considered. In particular, the expert evaluation method and the empirical analysis of factory technologists are used for a single index, and a recursive algorithm is given. According to the principle of the maximum membership degree, the merits and demerits based on the membership degree of the comments are evaluated, and both the multi-level index system and the fuzzy comprehensive evaluation method are combined. In addition, a comprehensive evaluation method, i.e. Multi-level Fuzzy Correlation Clustering Analysis (MFCCA) with a clear idea, is given for the low-carbon evaluation of the manufacturing products. Finally, the correctness, effectiveness and superiority of the proposed method are verified via the test experiments of an electric machine factory in the manufacturing industry. ARTICLE HISTORY

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Essential Motor Health Monitoring: Making Informed Decisions About Motor Maintenance Before a Failure Occurs

Cited by 11 publications

References 2 publications

A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems

A Fault Analysis Method for Three‐Phase Induction Motors Based on Spiking Neural P Systems

Industrial Data-Driven Monitoring Based on Incremental Learning Applied to the Detection of Novel Faults

A low-carbon evaluation method for manufacturing products based on fuzzy mathematics

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