VMD based trigonometric entropy measure: a simple and effective tool for dynamic degradation monitoring of rolling element bearing

Kumar, Anil; Gandhi, C.P.; Vashishtha, Govind; Kundu, Pradeep; Tang, Hesheng; Głowacz, Adam; Shukla, Rohit; Xiang, Jiawei

doi:10.1088/1361-6501/ac2fe8

Cited by 73 publications

(30 citation statements)

References 46 publications

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“…The determination of the mathematical description of the main signals components (sum of sinusoids model) by curve fitting can be successfully applied for condition monitoring purposes based on the gearbox vibration [ 49 , 50 ] (partially proved in Figure 43 ) or instantaneous angular speed evolutions (partially proved in Figure 38 ).…”

Section: Resultsmentioning

confidence: 99%

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

Horodincă

Bumbu

Chitariu

et al. 2023

Sensors

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This paper presents some advances in condition monitoring for rotary machines (particularly for a lathe headstock gearbox) running idle with a constant speed, based on the behaviour of a driving three-phase AC asynchronous induction motor used as a sensor of the mechanical power via the absorbed electrical power. The majority of the variable phenomena involved in this condition monitoring are periodical (machines having rotary parts) and should be mechanically supplied through a variable electrical power absorbed by a motor with periodical components (having frequencies equal to the rotational frequency of the machine parts). The paper proposes some signal processing and analysis methods for the variable part of the absorbed electrical power (or its constituents: active and instantaneous power, instantaneous current, power factor, etc.) in order to achieve a description of these periodical constituents, each one often described as a sum of sinusoidal components with a fundamental and some harmonics. In testing these methods, the paper confirms the hypothesis that the evolution of the electrical power (instantaneous and active) has a predominantly deterministic character. Two main signal analysis methods were used, with good, comparable results: the fast Fourier transform of short and long signal sequences (for the frequency domain) and the curve fitting estimation (in the time domain). The determination of the amplitude, frequency and phase at origin of time for each of these components helps to describe the condition (normal or abnormal) of the machine parts. Several achievements confirm the viability of this study: a characterization of a flat driving belt condition and a beating power phenomenon generated by two rotary shafts inside the gearbox. For comparison purposes, the same signal analysis methods were applied to describe the evolution of the vibration signal and the instantaneous angular speed signal at the gearbox output spindle. Many similarities in behaviour among certain mechanical parts (including their electrical power, vibration and instantaneous angular speed) were highlighted.

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Section: Resultsmentioning

confidence: 99%

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

Horodincă

Bumbu

Chitariu

et al. 2023

Sensors

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“…PCA is one of the statistical learning algorithms used to reduce the features extracted from a signal. It has always been adopted for prediction, classification, and feature extraction problems [38,39]. It changes a lot of related variables into new sets of uncorrelated variables and, in the interim, holds most of the information on the first signal.…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed

Pule

Matsebe

Samikannu

2022

Mathematical Problems in Engineering

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Vibration analysis is widely used as an efficient condition monitoring (CM) tool for rotating machines in various industries. Fault detection and diagnosis (FDD) models play an important role in the development of any CM system. The use of artificial intelligence (AI) has since gained recognition in the development of fault detection and diagnosis systems. In this paper, a combination of principal component analysis (PCA) which is used for reducing the data dimensionality, and support vector machine (SVM) which is adopted for classification to carry out fault detection and diagnosis of faults in bearings using vibrations. The diagnostic feature design and machine learning toolbox in MATLAB are used to develop features and train the models, respectively. Real data from the Mendeley data depository is used to test and evaluate the models. Model training is carried out using data with varying speeds representing different conditions of bearing making it different from similar approaches involving SVM. The choice of data used proves that SVM can be able to classify faults with consideration of the varying operating speeds. Results have shown that the combination of PCA and SVM is effective in fault diagnosis of bearing faults under varying speeds such that a 97.4% classification accuracy was achieved. The result implies that PCA and SVM can be implemented in various industrial setups where variable speeds can occur both intentionally or nonintentionally. Furthermore, the method was able to differentiate between compounding faults and faults that occur at different times. The confusion matrix further proves the quality and accuracy of the trained model. Future work will focus on the development of models that can carry out the prognosis of faults in bearings as well as to model for other faults other than bearing faults.

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“…One comes from the Centre for Intelligent Maintenance Systems, and the other from the XJTU-SY Bearing Databases. The suggested method has the ability to raise the alert about the beginnings of faults relatively at an early stage [23].…”

Section: Introductionmentioning

confidence: 99%

Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

et al. 2023

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Condition monitoring of wind turbines is progressively increasing to maintain the continuity of clean energy supply to power grids. This issue is of great importance since it prevents wind turbines from failing and overheating, as most wind turbines with doubly fed induction generators (DFIG) are overheated due to faults in generator bearings. Bearing fault detection has become a main topic targeting the optimum operation, unscheduled downtime, and maintenance cost of turbine generators. Wind turbines are equipped with condition monitoring devices. However, effective and reliable fault detection still faces significant difficulties. As the majority of health monitoring techniques are primarily focused on a single operating condition, they are unable to effectively determine the health condition of turbines, which results in unwanted downtimes. New and reliable strategies for data analysis were incorporated into this research, given the large amount and variety of data. The development of a new model of the temperature of the DFIG bearing versus wind speed to identify false alarms is the key innovation of this work. This research aims to analyze the parameters for condition monitoring of DFIG bearings using SCADA data for k-means clustering training. The variables of k are obtained by the elbow method that revealed three classes of k (k = 0, 1, and 2). Box plot visualization is used to quantify data points. The average rotation speed and average temperature measurement of the DFIG bearings are found to be primary indicators to characterize normal or irregular operating conditions. In order to evaluate the performance of the clustering model, an analysis of the assessment indices is also executed. The ultimate goal of the study is to be able to use SCADA-recorded data to provide advance warning of failures or performance issues.

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VMD based trigonometric entropy measure: a simple and effective tool for dynamic degradation monitoring of rolling element bearing

Cited by 73 publications

References 46 publications

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

On the Behaviour of an AC Induction Motor as Sensor for Condition Monitoring of Driven Rotary Machines

Application of PCA and SVM in Fault Detection and Diagnosis of Bearings with Varying Speed

Condition Assessment and Analysis of Bearing of Doubly Fed Wind Turbines Using Machine Learning Technique

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