Empirical Study of Features and Classifiers for Fault Diagnosis in Motorcycles Based on

Acoustic Signals

Veerappa B. Pagi, BEC BagalkotRamesh S. Wadawadagi, BEC Bagalkot

Basavaraj S. Anami, KLEIT Hubli

Presentation overview

Introduction Motivation & Objectives Literature review Features Used Approach Classifiers Used Results Conclusion

Introduction

• Vehicles generate dissimilar sound patterns in different working conditions.

• The sound patterns give a clue of the fault• Sound samples of vehicles with running engines can be

acquired and tested.• Acoustic features of the signal are computed and analyzed to

classify the faults.• It is observed that the diagnostic accuracy depends on usage,

maintenance, environmental and road conditions. • Overall classification accuracy upto 95% is achived, when

tested with various features and classifiers.

Motivation Non-speech sound recognition revolves around classifying the

musical instruments, environmental sound classification, machinery fault detection, etc.

Increasing market for two-wheelers in India. Two Wheelers sales registered growth of 8.09 percent in the financial year 2014-15.

Increasing road accidents due to faults in vehicles: Accident rate among males (83%).

No databases are available for two-wheeler vehicle sounds. Reported works classify the vehicles into trucks, wagons, cars,

and two-wheelers. At most fault detection is approached.

TABLE 1 A COMPARATIVE STUDY OF FAULT DIAGNOSIS TECHNIQUES

Sl.No Domain Features Classifier Performance (%)

1 TD Rough Set Theory Decision Table 50.9-100

2 TD WignerTrispectrum LS-SVM 92

3 TFD Empirical mode decomposition (EMD)

IMF-AR, SVM 100

4 WPA db4, 8, 20 ANN 91.25 - 99.02

5 WPA Entropy of 5 level db4 family

ANN 89.68 - 90.23

6 WPA PSD of the STFT K-NN, SVM 61.7 – 97.8

7 FD Statistical features NRMSE 90 - 100

8 FD One-third-octave freq. spectrum

BPNN 81.02 - 87.77

9 WPA Central and invariant contour moments of DB4 wavelets

DTW with Euclidean distance

98

10 TD PCA, LDA Bayesian 73.44 - 84.24

11 WPA DWT ANN 75 - 100

Challenges of the problem

Sensitive to Doppler Effect

Noise produced by moving parts

Noise due to atmospheric variations

Expensive and require extra hardware

Recording in ideal environment is far from real-world

situations

Methodology

Recording environment

Sony ICD-PX720 digital voice recorder, sampling frequency of 44.1 kHz with 16 bits quantization

Recorder held as per the recording standards

Feature Extraction l The segmented fault signature is analyzed using the time-domain,

frequency-domain, time-frequency domain and Wavelet Packet decomposition.

l Time-domain features such as Zero-Crossing Rate (ZCR), Short Time Energy (STE) and Root Mean Square (RMS) and Mel-frequency cepstral co-efficents (MFCC) are used to extract features from the signal.

l Mean and standard deviation of the spectral centroid are the frequency-domain features considered. Some features are extracted based on the pseudospectral estimate of the signal, which include the integration of the curve, chaincode, slope of the pseudospectrum and eigenvectors.

l Contour moments and energy distribution in the Wavelet packet subbands are also used as features.

1) Time domain features

1) Short-time energy:

Where x(m) is the input signal, w(n) is a window function.

2) Zero-crossing rate:

Where w(n) = N/2, 0≤ n≤ N-1.

3) Root mean square:

Where M is the total number of samples in processing window, x(m) is the value of the mth sample.

2) Frequency domain features

1)Spectrum Centroid:

Where x(m) is the input signal, w(M) is a window function and L is the window length.

The spectrum centroid is calculated as given in Equation (5).

3) Contour moments of DB4 wavelets

l The sound samples are subjected to wavelet decomposition using Daubechies db4 wavelets.

l The decomposition into approximation and detailed coefficients is carried out till eighth subband.

l The feature vector comprises of four 1D central contour moments (μ1, μ2, μ3 and μ5) and their invariants (F1, F2, F3 and F4), which are computed on approximation coefficients of a wavelet subband.

l The reference feature vector is computed using the mean of the feature vectors.

l The central moments and their invariants are computed over the approximation coefficients.

4) The slope of a pseudospectrum

5) Chain code of a pseudospectral segment

Where 1≤ i≤ 128

6) Feature reduction using Eigen vectorsThe 128-digit chain code is transformed into a matrix of size 16 x 8. The average of adjacent values in each row is taken to reduce the order of matrix into 8 x 8. The eigenvector of the reduced matrix is computed resulting in a feature vector of size 8.

7) Integration of pseudospectral segmentsThe trapezoidal rule approximates the region under the graph of the function f(x) as a trapezoid and calculating its area, as given by Equation (8).

8) Wavelet Packet EnergyThe coefficients are computed recursively for n levels of decomposition. Further, WPD produces 2n different sets of coefficients as opposed to (n +1) sets for the DWT. The percentage energy values of the first eight subbands form the feature vector.

9) Statistical featuresThe statistical features of the first eight subbands of db4 wavelet decomposition is used as the feature vector. The feature vector consists of the mean of the 1st subband, the median of the 2nd subband, variance of 3rd subband, standard deviation of 4th subband, minimum of 5th subband, maximum of 6th subband, mode of 7th subband and harmonic mean of 8th subband.

10) Feature extraction using MFCC

l Mel-frequency cepstral coefficients (MFCCs) are derived from a cepstral representation of the sound signal.

l The MFCCs are computed by taking the Fourier transform of a

signal.

l Then the powers of the spectrum are mapped onto the Mel

scale.

l The logs of the powers at each of the Mel-frequencies is

determined.

Classifier: ANN

Classifier: k-Means

Dynamic Time Warping (DTW)

The minimal distance matrix between the two sequences is computed using dynamic programming as in Equation (11).

The global warp cost of the two sequences is defined as given in Equation (12).

k-NN classifier

The three stages of operation

l Fault detection stagel Subsystem identification stagel Fault source localization stage

MATLAB version 7.11.0.584 (R2010b) is used for effective implementation. Disjoint sets of n percentage (n%) of the total samples is used for training, m percentage (m%) for validation and the remaining samples are used for testing.

Faults and database

Faults type Valve Setting problem (VS), Crank Fault (CF), Cylinder Kit problem (CK), Timing Chain problem (TC) Muffler Leakage (ML) and Silencer Leakage (SL)

886 sound samples, including healthy and faulty motorcycles

Results of stage 1: Fault detection stage

Results of stage 2: Faulty subsystem identification

Results of Stage 3: Fault localization in subsystem

Conclusionl The sound signals having lower SNRs are used in

experimentation, mainly due to work in real-world

environments.

l The classification accuracies are in the range of 70 to 100% for

all the stages.

l The kNN classifier is adjudged to be the most suitable for this

application due to its lower computational complexity.

l The work finds applications in fault source localization of

machinery, vehicles, musical instruments based on acoustic

signals.

Important references J. D. Wu, E.C. Chang, S.Y. Liao, J.M. Kuo & C.K. Huang, “Fault classification of a

scooter engine platform using wavelet transform and artificial neural network”, Proc. Int. Conf. Engineers and Computer Scientists, IMECS 2009, Hong Kong, March 18-20, Vol.1, 2009, pp58-63.

Wei Liao, Pu Han & Xu Liu, “Fault diagnosis for engine based on EMD and wavelet packet BP neural network”, 3rd Int. Symp. Intelligent Information Technology Application, 2009, pp672-676.

Jian-Da Wu, Jian-Bin Chain, Chen-Wei Chung, & Hao Yu, “Fault Analysis of Engine Timing Gear and Valve Clearance Using Discrete Wavelet and a Support Vector Machine”, Int. Journal of Computer Theory and Engineering, Vol.4, No.3, June 2012, pp386-390.

Jian-Da Wu & Chiu-Hong Liu, “Investigation of engine fault diagnosis using discrete wavelet transform and neural network”, Elsevier J. Expert Systems with Applications, 35, 2008, pp1200–1213.

Zhang Junhong & Han Bing, “Analysis of engine front noise using sound intensity techniques”, Mechanical Systems and Signal Processing, Vol.19, 2005, pp213-221.

Thanks

Auto experts of Bagalkot city

Principal of our college

My research guide

My colleague

Anonymous reviewers of our research papers