A Comprehensive Review of Condition Based Prognostic Maintenance (CBPM) for Induction Motor

Kumar, Sanjay; Mukherjee, Debottam; Guchhait, Pabitra Kumar; Banerjee, Ramashis; Srivastava, Ankit; Vishwakarma, D. N.; Saket, R. K.

doi:10.1109/access.2019.2926527

Cited by 83 publications

(52 citation statements)

References 98 publications

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“…Computational intelligence and machine learning (ML) are promising solutions for smart condition monitoring in real-world industrial applications [2], since these methods can learn from historical data and are suitable for processing high dimensional data from multiple sources. Condition monitoring itself is an interesting application in industry 4.0, as it allows to detect automatically incipient machine failures [3]. It is, therefore, an enabling technology for condition-based maintenance (CBM), an industrial predictive maintenance paradigm, which allows to schedule maintenance operations on an on-demand basis.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

König

Helmi

2020

Sensors

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Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system’s design to decide timely on the required sensors.

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Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

König

Helmi

2020

Sensors

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“…Several studies have been conducted on fault diagnosis and condition monitoring of BLDC motor's stator related faults. MCSA has been the most popular technique to diagnose stator faults in motors [5]. Motor current carries significant information about the precision of stator winding operation [6].…”

Section: A Motivationmentioning

confidence: 99%

An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals

Shifat

Hur

2020

IEEE Access

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Electric motor is a prominent rotary machinery in many engineering applications due to its excellent electrical energy utilization. With the increased demand in production and complex operating conditions, motors often run in a severe loading condition. Overload, overheating and many other intricate operating conditions account for the stator related faults in motors. Motor current signature analysis (MCSA) and vibration analysis have been popular techniques to diagnose different stator and rotor related faults in motors. But it is difficult to find the fault magnitude or fault threshold by using only one approach due to nonstationary motor operations. This paper presents a comprehensive review of a permanent magnet brushless DC motor's (BLDC motor) fault diagnosis combining vibration and current signals collected from sensors. Since the insulation break in the stator winding is the most commonly occurring fault in the stator, a short-circuit was artificially created between two windings. Based on the motor operating conditions, three health states are chosen from the experimental sensor data with different fault magnitudes. Health states are labeled as healthy state, incipient failure state, and severe failure state. Two effective fault diagnosis indices named kurtosis and third harmonic of motor current are selected for analyzing the vibration signals and current signals, respectively. Proposed diagnostics framework is validated using experimental data and proven to detect the stator fault at the early stage as well as distinguish between different fault states. Monitoring both mechanical and electrical characteristics of BLDC motor provides a thorough understanding of fault magnitude and threshold in different health states.

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“…When the index exceeds the critical point of the performance state, the resource allocation scheme of the next performance quality state will be automatically triggered. As shown in FIGURE 2, point (20,71) indicates that the performance quality of the system has entered a hazardous state. Intervention measures such as load reduction and temporary shutdown must be initiated to prevent the further deterioration of the performance quality and the occurrence of severe production accidents.…”

Section: -3 --mentioning

confidence: 99%

“…Jamshidi et al [15] proposed a decision support approach for CBM of rails that relies on expertbased systems. Reviews on CBM can be found in [16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

Wang

Gao

et al. 2020

IEEE Access

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Scientific and effective supportability mechanisms have a profound impact on large-scale electromechanical systems and improve their performance quality, while decreasing costs. However, the contradiction between the static supportability strategy and time-varying performance quality conditions remains not sufficiently resolved. To solve this problem, this paper proposes a performance quality condition-based dynamic supportability mechanism. First, model-driven risk identification and control chart-based pattern recognition were used to trigger the dynamic allocation of the limit supportability resource. Second, the performance quality mode and effect analysis and the multiagent collaborative chain were defined to identify the resource lists of maintenance activities. Third, a general control framework was constructed to evaluate the compliance and applicability of maintenance activities. This study formulated a dynamic supportability mechanism for performance quality with a closed loop of activity triggering, resource guarantee, and maintenance effectiveness evaluation. As an expansion and improvement of condition-based maintenance, the dynamic supportability mechanism overcomes the several drawbacks of the existing supportability mechanisms of large-scale electromechanical systems. The proposed mechanism also advances the integration of management and technology in comprehensive supportability. Therefore, the proposed mechanism and methods can be flexibly and efficiently used for the dynamic supportability of electromechanical systems. Moreover, this study provides insights into risk identification, macro pattern recognition and process control and can be used for other engineering applications.

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A Comprehensive Review of Condition Based Prognostic Maintenance (CBPM) for Induction Motor

Cited by 83 publications

References 98 publications

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models

An Effective Stator Fault Diagnosis Framework of BLDC Motor Based on Vibration and Current Signals

Condition-Based Dynamic Supportability Mechanism for the Performance Quality of Large-Scale Electromechanical Systems

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