Audio Signature-Based Condition Monitoring of Internal Combustion Engine Using FFT and Correlation Approach

A unique technique is proposed based on sparseautoencoders for automated fault detection and classification using the acoustic signal generated from internal combustion (IC) engines. This technique does not require any handengineered feature extraction and feature selection from acoustic data for fault detection and classification, as usually done. The proposed technique uses sparse-autoencoder for unsupervised features extraction from the training data. The training and testing data sets are then transformed by these extracted features, before being used by the softmax regression for classification of unknown engines into healthy and faulty class. The use of sparse-autoencoder to learn fault features improves the classification performance significantly with a small number of training data. This technique is tested on industrial IC engine data set, with overall classification performance of 183 correct classifications out of 186 test cases for four different fault classes.

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“…Table 1 shows the seeded faults and number of data sets recorded for each fault type. The details of each fault are described in [4,20,21].…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Fault detection and classification by unsupervised feature extraction and dimensionality reduction

Chopra

Yadav

2015

Complex Intell. Syst.

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“…Table 1 shows the seeded faults and number of data sets recorded for each fault type. The details of each fault are described in [4,20,21]. There are total 248 labeled data sets that are recorded for each sensor position.…”

Section: Organization Of Data and Testing Proceduresmentioning

confidence: 99%

“…Yadav et al [20] have proposed an FFT and correlationbased technique using acoustic data from the same type of IC Engine Test Rig. In this work, the final classification accuracy for four different types of fault classes was less than 93%.…”

Section: Comparison With Existing Techniquesmentioning

confidence: 99%

Restricted Boltzmann machine and softmax regression for fault detection and classification

Chopra

Yadav

2017

Complex Intell. Syst.

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A unique technique is proposed based on restricted Boltzmann machine (RBM) and softmax regression for automated fault detection and classification using the acoustic signal generated from IC (Internal Combustion) engines. This technique uses RBM for unsupervised fault feature extraction from the frequency spectrum of the noisy acoustic signal. These extracted features are then used to reduce the dimensionality of the training and testing data vectors. These reduced dimensionality data vectors are used by softmax regression-based classifier for classification of the engine into faulty and healthy class. The proposed technique does not require any hand-engineered feature extraction, as usually done. This technique performs very well with a small number of training data. The overall performance of this technique for four different fault classes is more than 99% on the industrial IC engine data. In a typical case, with only 38 training data sets and 210 test data sets, the performance is 99.52%.

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“…Machinery condition based monitoring (CBM) [1], [2], fault and failure classification (FC) [3], [4], and remaining useful life(RUL) [6] are challenges that are gaining much interest by both academic and industrial communities. Table I [5] shows the expected improvements of using automatic or semiautomatic systems for condition monitoring, where one can observe a significant improvement on maintenance costs and total productivity, which are of great interest for the industry.…”

Section: Introductionmentioning

confidence: 99%

Audio anomaly detection on rotating machinery using image signal processing

Prego

Lima

Netto

et al. 2016

2016 IEEE 7th Latin American Symposium on Circuits &Amp; Systems (LASCAS)

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This paper addresses the problem of anomaly detection on rotating machinery in industrial environments using single channel audio signals. The proposed algorithm is based on image processing feature analysis obtained from the image representation of the Short-time Fourier Transform of reference and degraded audio signals. In order to assess the potential of the algorithm, a 8 signals database is recorded. The proposed algorithm is able to separate signals of machinery normal behavior from signals of machinery anomalous behavior with 100% hit rate using the recorded database.

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Audio Signature-Based Condition Monitoring of Internal Combustion Engine Using FFT and Correlation Approach

Cited by 61 publications

References 22 publications

Fault detection and classification by unsupervised feature extraction and dimensionality reduction

Fault detection and classification by unsupervised feature extraction and dimensionality reduction

Restricted Boltzmann machine and softmax regression for fault detection and classification

Audio anomaly detection on rotating machinery using image signal processing

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