Engine fault diagnosis using sound analysis

Kemalkar, Aniket K.; Bairagi, Vinayak K.

doi:10.1109/icacdot.2016.7877726

Cited by 22 publications

(9 citation statements)

References 8 publications

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“…Low Pass filtering Normalization F 1 The average rate at which information is produced by a stochastic source of data is called Entropy [14]. Entropy related with each promising data value is the negative logarithm of the probability mass function for the value:…”

Section: Preprocessing Extreme Learning Neural Network Classifiermentioning

confidence: 99%

“…The Engine is the heart of automobiles and may get a complaint if the rider doesn't make continuous checking of the engine and other particles. Viewing from the side of engine complaints, the sound of the engine would provide major information about the engine problem [1]. Vehicles of a particular type, in various working environments, produce different sound patterns.…”

Section: Introductionmentioning

confidence: 99%

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Sound Signal Based Fault Classiﬁcation System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classiﬁers

Jayasree¹,

Ananth²

2020

Sound&Vibration

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Vehicles generate dissimilar sound patterns under different working environments. These generated sound patterns signify the condition of the engines, which in turn is used for diagnosing various faults. In this paper, the sound signals produced by motorcycles are analyzed to locate various faults. The important attributes are extracted from the generated sound signals based on time, frequency and wavelet domains which clearly describe the statistical behavior of the signals. Further, various types of faults are classified using the Extreme Learning Machine (ELM) classifier from the extracted features. Moreover, the improved classification performance is obtained by the combination of feature sets in different domains. The simulation results clearly demonstrate that the proposed hybrid feature set together with the ELM classifier gives more promising results with higher classification accuracy when compared with the other conventional methods.

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Section: Preprocessing Extreme Learning Neural Network Classifiermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sound Signal Based Fault Classiﬁcation System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classiﬁers

Jayasree¹,

Ananth²

2020

Sound&Vibration

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“…However, their method did not show a promising result when applied in our drill sound dataset because each sound recording is extremely short. Kemalkar et al [5] extracted MFCCs features and made a comparison between these features and a library of features to decide on the fault or non-fault state of a bike engine. Ning Zhang [6] used the principal component analysis (PCA) algorithm to extract and train the training samples.…”

Section: Introductionmentioning

confidence: 99%

Drill Fault Diagnosis Based on the Scalogram and Mel Spectrogram of Sound Signals Using Artificial Intelligence

Tran

Lundgren

2020

IEEE Access

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“…A technique using Mel-frequency Cepstrum Coefficients (MFCCs) from audio signals with SVM for classifications was presented in [16]. The use of MFCCs features and their comparison with a library of features to determine if a bike engine is healthy or malfunctioning was proposed in [17]. In contrast, [18] developed an ML-based fault identification and diagnosis technique for electric current signals based on a distinct feature selection, extraction, and infusion procedure.…”

Section: Introductionmentioning

confidence: 99%

Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM)

Rohan

Raouf

Kim

2020

Sensors

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In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%.

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Engine fault diagnosis using sound analysis

Cited by 22 publications

References 8 publications

Sound Signal Based Fault Classiﬁcation System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classiﬁers

Sound Signal Based Fault Classiﬁcation System in Motorcycles Using Hybrid Feature Sets and Extreme Learning Machine Classiﬁers

Drill Fault Diagnosis Based on the Scalogram and Mel Spectrogram of Sound Signals Using Artificial Intelligence

Rotate Vector (RV) Reducer Fault Detection and Diagnosis System: Towards Component Level Prognostics and Health Management (PHM)

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