&lt;b&gt;Abnormality Detection for Auxiliary Drive Shafts on Diesel Cars Using Vibration Condition Monitoring&lt;/b&gt;

Kondo, Minoru; Takashige, T

doi:10.2219/rtriqr.59.1_15

Cited by 3 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, it is known that vibration monitoring is effective for monitoring the condition of equipment. Therefore, RTRI is developing vibration monitoring systems for vehicle equipment [1][2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

<b>Fast Calculation Method of Abnormality Degree for Real Time Abnormality Detection in Vehicle Equipment</b>

Kondo¹,

Sakaidani²

2020

QR of RTRI

Self Cite

View full text Add to dashboard Cite

Vibration monitoring is effective for early detection of equipment failure. In the vibration monitoring system proposed in this paper, abnormality detection is performed by applying the nearest neighbor method (NN) to the octave band analysis results of vibration. However, the NN requires a long calculation time and is not suitable for detecting abnormalities in real time. Therefore, applying the One Class Support Vector Machine (OCSVM) to abnormality detection was considered. In this paper, the OCSVM was applied to actual vibration data, and the calculation time was compared with those of the NN. The result shows that the calculation time is significantly reduced compared to the NN approach.

show abstract

Section: Introductionmentioning

confidence: 99%

<b>Fast Calculation Method of Abnormality Degree for Real Time Abnormality Detection in Vehicle Equipment</b>

Kondo¹,

Sakaidani²

2020

QR of RTRI

Self Cite

View full text Add to dashboard Cite

show abstract

Bearing Fault Detection for Railway Traction Motors Through Leakage Current

Sakaidani

Kondo

2018

2018 XIII International Conference on Electrical Machines (ICEM)

View full text Add to dashboard Cite

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

View full text Add to dashboard Cite

Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

show abstract

<b>Abnormality Detection for Auxiliary Drive Shafts on Diesel Cars Using Vibration Condition Monitoring</b>

Cited by 3 publications

References 1 publication

<b>Fast Calculation Method of Abnormality Degree for Real Time Abnormality Detection in Vehicle Equipment</b>

<b>Fast Calculation Method of Abnormality Degree for Real Time Abnormality Detection in Vehicle Equipment</b>

Bearing Fault Detection for Railway Traction Motors Through Leakage Current

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Contact Info

Product

Resources

About