Optimized SWPT and Decision Tree for Incipient Bearing Fault Diagnosis

Abid, Firas Ben; Sallem, Marwen; Braham, Ahmed

doi:10.1109/sta.2019.8717197

Cited by 10 publications

(5 citation statements)

References 19 publications

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“…The use of decision trees for rolling element bearing diagnostics based on time domain features of vibration signal is also frequently used. This topic is discussed in the works [1,3,22,36]. The obtained results allow effective and fast technical condition diagnosis, but they are not verified in the conditions of real car operation.…”

Section: Related Work and Other Methodsmentioning

confidence: 99%

“…where: p W -set of technical conditions for traction transmission diagnostics, 1 w -class of good technical conditions, 1 n w -class of average technical conditions (permissible), 0 w -class of bad technical conditions. The 1 w technical condition means that the value of the diagnostic parameter y did not reach and did not exceed the upper limit value Sg.…”

Section: Diagnostic Parameter Limit Valuementioning

confidence: 99%

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Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system

Nowakowski¹,

Komorski²

2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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Currently, one of the trends in the automotive industry is to make vehicles as autonomous as possible. In particular, this concerns the implementation of complex and innovative selfdiagnostic systems for cars. This paper proposes a new diagnostic algorithm that evaluates the performance of the drive shaft bearings of a road vehicle during use. The diagnostic parameter was selected based on vibration measurements and machine learning analysis results. The analyses included the use of more than a dozen time domain features of vibration signal in different frequency ranges. Upper limit values and down limit values of the diagnostic parameter were determined, based on which the vehicle user will receive information about impending wear and total bearing damage. Additionally, statistical verification of the developed model and validation of the results were performed.

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Section: Related Work and Other Methodsmentioning

confidence: 99%

Section: Diagnostic Parameter Limit Valuementioning

confidence: 99%

Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system

Nowakowski¹,

Komorski²

2022

Eksploatacja i Niezawodność – Maintenance and Reliability

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show abstract

“…The analysis of the vibration signal in the frequency domain (e.g., determining the failure frequency of characteristic rolling bearing components [ 17 ]) is an effective way to detect bearing malfunctions without disassembly. In the study reported in [ 18 ], the authors presented the use of decision trees for rolling element bearing diagnostics based on selected vibration signal characteristics. The paper [ 2 ] presents a unified methodology for incremental learning of new information from evolving databases.…”

Section: Related Work and Other Methodsmentioning

confidence: 99%

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

Prażnowski

Mamala

Deptuła

et al. 2023

Sensors

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This paper presents a method for the multi-criteria classification of data in terms of identifying pneumatic wheel imbalance on the basis of vehicle body vibrations in normal operation conditions. The paper uses an expert system based on search graphs that apply source features of objects and distances from points in the space of classified objects (the metric used). Rules generated for data obtained from tests performed under stationary and road conditions using a chassis dynamometer were used to develop the expert system. The recorded linear acceleration signals of the vehicle body were analyzed in the frequency domain for which the power spectral density was determined. The power field values for selected harmonics of the spectrum consistent with the angular velocity of the wheel were adopted for further analysis. In the developed expert system, the Kamada–Kawai model was used to arrange the nodes of the decision tree graph. Based on the developed database containing learning and testing data for each vehicle speed and wheel balance condition, the probability of the wheel imbalance condition was determined. As a result of the analysis, it was determined that the highest probability of identifying wheel imbalance equal to almost 100% was obtained in the vehicle speed range of 50 km/h to 70 km/h. This is known as the pre-resonance range in relation to the eigenfrequency of the wheel vibrations. As the vehicle speed increases, the accuracy of the data classification for identifying wheel imbalance in relation to the learning data decreases to 50% for the speed of 90 km/h.

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“…It is, however, prone to overfitting and underfitting issues, which can be alleviated in some cases by pruning [2,27]. DT has been used in a variety of studies for regression and classification problems; for example, in [28], the authors combined DT with Optimized Stationary Wavelet Packet Transform for incipient bearing fault diagnosis. RF is based on the grouping of trees for regression and classification and is thought to mitigate underfitting and overfitting, both of which are common problems in DT [2,9,19].…”

Section: Review Of the Other Ml-based Classification Algorithmsmentioning

confidence: 99%

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

Okwuosa

Hur

2022

Energies

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In most manufacturing industries, squirrel cage induction motors (SCIMs) are essential due to their robust nature, high torque generation, and low maintenance costs, so their failure often times affects productivity, profitability, reliability, etc. While various research studies presented techniques for addressing most of these machines’ prevailing issues, fault detection in cases of low slip or, low load, and no loading conditions for motor current signature analysis still remains a great concern. When compared to the impact on the machine at full load conditions, fault detection at low load conditions helps mitigate the impact of the damage on SCIM and reduces maintenance costs. Using stator current data from the SCIM’s direct online starter method, this study presents a feature engineering-aided fault classification method for SCIM at minor-load conditions based on a filter approach using the support vector classification (SVC) algorithm as the classifier. This method leverages the loop-hole of the Fourier Transform at minor-load conditions by harnessing the uniqueness of the Hilbert Transform (HT) to present a methodology that combines different feature engineering technologies to excite, extract, and select 10 discriminant information using a filter-based approach as the selection tool for fault classification. With the selected features, the SVC performed exceptionally well, with a significant diagnostic performance accuracy of 97.32%. Further testing with other well-known robust classifiers such as decision tree (DT), random forest (RF), k-nearest neighbor (KNN), gradient boost classifier (GBC), stochastic gradient descent (SGD), and global assessment metrics revealed that the SVC is reliable in terms of accuracy and computation speeds.

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Optimized SWPT and Decision Tree for Incipient Bearing Fault Diagnosis

Cited by 10 publications

References 19 publications

Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system

Diagnostics of the drive shaft bearing based on vibrations in the high-frequency range as a part of the vehicle's self-diagnostic system

Diagnosis of the Pneumatic Wheel Condition Based on Vibration Analysis of the Sprung Mass in the Vehicle Self-Diagnostics System

A Filter-Based Feature-Engineering-Assisted SVC Fault Classification for SCIM at Minor-Load Conditions

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