Bearing fault detection using PCA and Wavelet based envelope analysis

Chopade, Smita A.; Gaikwad, Jitendra A.; Kulkarni, Jayant

doi:10.1109/icatcct.2016.7912002

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…The bearing is housed inside a bearing case on the shaft at the front of the motor. Three types of bearing conditions-normal bearing condition, outer race defect, and inner race defect-are explored here by changing the roller bearing in the electric motor [5]. As a test bearing, SKF 6203 2RS bearing is utilized.…”

Section: Methods and Materials A Experimental Setupmentioning

confidence: 99%

Comparison of Signal Processing Methods for Bearing Fault Detection

Gaikwad,

Patankar,

Kamalapure

et al. 2023

IJRASET

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Bearings with rolling elements are a typical part of rotating machinery. Vibration analysis is one of them that is frequently used to gauge the general wellbeing and condition of rotating machinery. Finding bearing defects is important in the pursuit of extremely reliable operations. The main relationship between vibration signal-based features and measured signals is that they can be used in order to assess the condition of a bearing. In order to examine the effectiveness of non-parametric harmonic approaches for defect identification using real motor data, the study recommends utilizing Thomson's Multitaper Estimation and Welch Periodogram Estimation. The researches show that this approach can identify outer race faults and inner race fault. The conclusive evidence points to Thomson's Multitaper Periodogram Estimation method as a successful technique for bearing fault identification.

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Section: Methods and Materials A Experimental Setupmentioning

confidence: 99%

Comparison of Signal Processing Methods for Bearing Fault Detection

Gaikwad,

Patankar,

Kamalapure

et al. 2023

IJRASET

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“…LITERATURE REVIEW Smita A. Chopade et al [1] in this study, vibration analysis is used to detect bearing faults. When a bearing fails, some impact force is generated.…”

Section: IImentioning

confidence: 99%

Bearing Fault Detection using Vibration Analysis

Gaikwad,

Mundhada,

Nagre

et al. 2024

IJRASET

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In the current scenario, condition monitoring is an essential technique for the maintenance of machinery. Various methods such as vibration analysis, temperature analysis, and wear and debris analysis have been introduced, with vibration analysis proving to be the most effective for detecting machine faults. This study focuses on bearing fault detection using vibration analysis. Bearing faults generate impact forces that alter vibration patterns. We examined the vibration patterns of a 0.25HP PMDC motor under different load conditions and at varying RPMs, comparing good and faulty bearings to detect inner and outer race bearing faults. Inner race defect detection is challenging under poor signal-to-noise ratio (SNR) conditions due to the vibration signal being masked by other vibrations. To enhance SNR, Principal Component Analysis (PCA) was utilized, and Artificial Neural Networks (ANN) algorithms were employed. Rotating machinery, widely used in industries such as petroleum, automotive, HVAC, and food processing, relies on bearings for rotational or linear movement, reducing friction and stress. Rolling Element Bearings (REBs) offer a favorable balance of friction, lifetime, stiffness, speed, and cost. Thus, real-time monitoring and diagnosis of bearings are critical to prevent failures, enhance safety, avoid unforeseen production downtime, and reduce costs. This study proposes an approach based on Wavelet Transform and ANN to analyze vibration signals from rolling element bearings and to identify and classify component defects.

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“…This can cause the motor's current consumption as well as vibration to change. In the works of Chopade et al [13], Ozcan et al [14], and Shao et al [15], bearing defects were simulated by placing loads on the motor shaft, so that there is stress on the bearing. This made it higher for the shafts to move and caused changes in current consumption as well as vibration.…”

Section: B Experimental Setupmentioning

confidence: 99%

Anomaly Detection System for Stepper Motors

Sharrar¹

2022

IJERECE

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Predictive maintenance (PdM) systems have the potential to autonomously detect underlying motor issues at early stages. Although many such systems have been proposed up to data, they have yet to be implemented. Most of these methods, which are based on supervised learning, require hours of manual data collection and annotation. Furthermore, they are mostly made to tackle a single instead of the multiple motor issues that may occur and are unable to adapt to varying motor speed and load conditions. Thus, they are not viable for industrial implementation. Therefore, this paper presents an unsupervised LSTM autoencoder-based anomaly detection system for electric motors. It analyzes the vibration and current consumption data from motors to detect anomalies, which is sufficient to account for the various motor defects. The system comes with a variety of features that allows users to autonomously collect data, train models and deploy models. In addition to that, users can remotely keep track of the motor’s conditions. To test the system, a hardware test bench using a stepper motor is made to simulate defective conditions. The LSTM Autoencoder-based anomaly detection system is described step-by-step in this paper.

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Bearing fault detection using PCA and Wavelet based envelope analysis

Cited by 7 publications

References 9 publications

Comparison of Signal Processing Methods for Bearing Fault Detection

Comparison of Signal Processing Methods for Bearing Fault Detection

Bearing Fault Detection using Vibration Analysis

Anomaly Detection System for Stepper Motors

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