An Intelligent Fault Diagnosis of Induction Motors in an Arbitrary Noisy Environment

Irfan, Muhammad; Saad, Nordin; Ibrahim, Rosdiazli; Asirvadam, Vijanth S.; Magzoub, Muawia A.

doi:10.1007/s10921-015-0327-3

Cited by 13 publications

(5 citation statements)

References 42 publications

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“…30–34 Several papers have been published in the past decade to improve the performance of the MCA by developing pre-processing algorithms to make the condition monitoring system reliable, efficient, economical, and to reduce the complexity. 35–38 Although MCA is a better alternative to intrusive techniques but harmonics associated with defects are suppressed by the amplitude of line frequency and give a false alarm. Another issue reported with MCA is the high tendency of false alarms in a highly noisy environment.…”

Section: Introductionmentioning

confidence: 99%

Condition monitoring of water pump bearings using ensemble classifier

Irfan

Althobiani

Alwadie

et al. 2022

Advances in Mechanical Engineering

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The bearings faults are reported to be the major reason for centrifugal pump (CPs) failures. Limited literature is available to diagnose the minor scratches in the bearing surface through non-intrusive condition monitoring techniques. Recent research on the analysis of bearing scratches through non-intrusive motor current analysis (MCA) has shown encouraging results where the comparison of machine learning and convolutional neural networks (CNNs) was performed in the classification of healthy bearings and faulty bearings (holes and scratches). The fault classification accuracy of 89.26% through MCA combination with machine learning and CNN algorithm was reported which is very low. The key factors of low accuracies were identified as low amplitudes of the harmonics in the MCA spectrum, the magnitude of environmental noise, and utilization of conventional feature extraction techniques. This problem has been tackled in this paper by developing a novel feature extractor (NFE) that extracts powerful features from the integrated current and voltage sensors data. The NFE has been derived using the threshold-based decision mechanism which has the capability to identify the location of the feature harmonic, feature extraction, measure the amplitude of the fault component, and compare it with the derived threshold. The experimental data has been collected for the bearing balls (BB), bearing cage (BC), inner race (IR) and the outer race (OR) faults, and the performance of the NFE has been tested on an ensemble classifier (CatBoost) and the better classification accuracy (99.2% for an individual feature and 100% with the combination of two or more features) of NFE has been achieved as compared to previously reported methods.

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Section: Introductionmentioning

confidence: 99%

Condition monitoring of water pump bearings using ensemble classifier

Irfan

Althobiani

Alwadie

et al. 2022

Advances in Mechanical Engineering

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“…A lot of research has been conducted in the past on the diagnosis of holes or dents in the bearing raceways and it has been shown that a small-sized hole (diameter < 1 mm) can produce tinny amplitudes; it is thus a great challenge to reliably diagnose these small faults [ 26 , 27 , 28 , 29 , 30 ]. Some studies have used noise filtration and threshold-based statistical analysis algorithms to enhance the reliability of the small-sized hole diagnostics [ 26 , 27 ]. Although there is a huge literature available on the diagnostics of hole-type faults in the bearing, very little research has been conducted on diagnosing the initial stage of these faults that appears in the form of scratches [ 28 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings

Irfan

Alwadie

Głowacz

et al. 2021

Sensors

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The reliable and cost-effective condition monitoring of the bearings installed in water pumps is a real challenge in the industry. This paper presents a novel strong feature selection and extraction algorithm (SFSEA) to extract fault-related features from the instantaneous power spectrum (IPS). The three features extracted from the IPS using the SFSEA are fed to an extreme gradient boosting (XBG) classifier to reliably detect and classify the minor bearing faults. The experiments performed on a lab-scale test setup demonstrated classification accuracy up to 100%, which is better than the previously reported fault classification accuracies and indicates the effectiveness of the proposed method.

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“…This maintenance strategy was also not optimal, as some parts were replaced while they were still in good working condition. To rectify these issues, plant engineers introduced the predictive maintenance concept, where the machine parts, performance, and health were monitored continuously, and maintenance was preplanned before the breakdowns [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Design of a Novel Electric Diagnostic Technique for Fault Analysis of Centrifugal Pumps

2019

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Centrifugal pumps are the fundamental components of most industries. They are used in almost every industry to transfer liquid through pipes. The breakdown of a pump causes heavy production losses, and hence, the development of an economical and user-friendly condition monitoring system is vital in order to estimate the health of a pump in a timely manner, and to avoid an unscheduled breakdown. The intrusive condition monitoring techniques (such as vibration analysis and acoustic emission) developed for the fault diagnosis of pumps utilize expensive vibration sensors, and these sensors need to be installed on the pump body for data collection. Non-intrusive techniques (such as motor current analysis) have been proven to be economical, but have limited capabilities for diagnosing the incipient faults in pumps operating in a noisy industrial environment. The electric diagnostic technique (EDT) proposed in this paper does not require the purchase of extra sensors, and instead utilizes the existing sensors, which are usually installed on the machines, to measure and display the motor line current and voltage. The EDT has been developed in the Laboratory Virtual Instrument Engineering Workbench (LabVIEW) so as to measure the three-phase line current, and then transform it into two-phase d–q currents. These d–q currents are plotted as patterns, and the statistical features of these patterns are used to segregate the centrifugal pump fault types. Detailed experiments and evaluations have been performed in order to check the viability of the developed EDT technique.

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An Intelligent Fault Diagnosis of Induction Motors in an Arbitrary Noisy Environment

Cited by 13 publications

References 42 publications

Condition monitoring of water pump bearings using ensemble classifier

Condition monitoring of water pump bearings using ensemble classifier

A Novel Feature Extraction and Fault Detection Technique for the Intelligent Fault Identification of Water Pump Bearings

Design of a Novel Electric Diagnostic Technique for Fault Analysis of Centrifugal Pumps

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