[IEEE 2010 Third International Workshop on Advanced Computational Intelligence (IWACI) - Suzhou, China (2010.08.25-2010.08.27)] Third International Workshop on Advanced Computational Intelligence - Intelligent diagnosis algorithm of power equipment based on acoustic signal processing

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  • AbstractThe operational state determination of power

    equipment is a key prerequisite to realize maintenance. On studying the relationship between power equipment state and its acoustic wave mutation character, a new diagnosis scheme of power equipment fault has been put forward. After the running acoustic signal acquired, MFCC coefficient has been selected the acoustic signal various band energy feature, and dynamic time warping (DTW) is utilized to determine equipment type. Then local energy band based wavelet packet decomposition is used in fault feature extraction. According to these feature parameters values and expert experience scoring, the knowledge based of fault database was established to diagnosis power equipment state and its fault level. Lastly, By 200 group transformer measured acoustic signal analysis experiments have been completed, and the results show the series acoustic treatment of methods is effective, and the diagnosis scheme of equipment failures have great practical value.

    I. INTRODUCTION OWER equipment noise (acoustic signal) is one kind of compound signal with stochastic process characteristic on generally condition. Some acoustic signal time-frequency

    changing has close connection with power equipments active state. Despite the different type transformer when load changes, the sound makes a difference, but the acoustic signal spectrum is a certain law. The acoustic signal is stable with equipment running safely, but the signal would have mutation when some accidents happen. In fault diagnosis techniques, acoustic recognition as one of the important methods was used in mechanical equipments fault diagnosis and observation system, as in [1]. In this article, we put forward a new method to analyze power equipments running state based on acoustic wave identifying. It is unwanted to touch high-voltage equipments body, and without consider surrounding electromagnetic contributions. So as a diagnose

    This work was supported in part by Doctor Degree Foundation of NCEPU Shutao Zhao is with Key Laboratory of Power System Protection and

    Dynamic Security Monitoring and Control under Ministry of Education, North China Electric Power University, Baoding 071003, Hebei Province, China. (corresponding author: 86-312-7522350; fax: 86-0312-7522252; e-mail: shutaozhao@163.com ).

    Baoshu Li is with Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control under Ministry of Education, North China Electric Power University, Baoding 071003, Hebei Province, China. (e-mail: Baoshuli_bd@yahoo.com.cn)

    Yumin Ge is with Key Laboratory of Power System Protection and Dynamic Security Monitoring and Control under Ministry of Education, North China Electric Power University, Baoding 071003, Hebei Province, China. (e-mail: gyumin@163.com )

    Weiguo Tong is with the shool of Control Science and Engineering, North China Electric Power University, Baoding 071003, Hebei Province, China. (e-mail: twg1018@163.co m)

    criterion of power equipments running state, acoustic recognition has unique dominance, as in [2], [3].

    Utilizing enough integrating sound signal of power equipments, acoustic wave change and equipment running state linkages regularity would be studied, and its characters can be extracted to diagnosis the equipments running state. But in actual operation of diagnosis system, because of the equipments structure complexity and the mutual interference between each kind of other acoustic source, it exist many problems which demand prompt solution, acoustic signal processing is the committed step , as in [4],[5].

    In this article, system can be established, to judge the sound source first from the dynamic time warping (DTW) method. After that using wavelet theory and expert system to identify weather the transformer is running abnormal and what kind of failure it happens. From this method, automation level of the power system will be improved. It will bring about great technological progress for construction of the modern intelligent electric network.

    II. ACOUSTIC BASED EQUIPMENT FAULT DIAGNOSIS Each power equipments running and fault state has its own

    specially acoustic signal symptoms. Being reflected in signal power spectrum is some specially amount of confidence peaks and its correspondence frequencies. Several sonic transducer have been installed near the power equipment, via acquisition and storage, computer can collect power equipments running sound. Because of background wave, the acoustic signal has complex ingredients and abundant contents, the acoustic signal identify system is difference clearly with other pattern recognition. Fig. 1 is the principle block diagram of the acoustic recognition system.

    Through sonic transducer and sampling card, the acoustic signal from the power equipment is discrete acquired, via preprocess, and the spectrum features can be calculated, comparison with the reference mode database, the fault and its attribute can be derived. The power equipment running state can be determined. This identify system includes feature extraction, pattern matching and reference mode database units. Acoustic signals and its spectrum feature must be statistic, inductive and comprehensive analysis, and then the frequency spectrum regularity would be derived from large quantity normal and improper signals. The feature database is established with computer, this is the reference pattern identification database.

    But in reality, to obtain every kind of power equipments fault feature is rather difficult. To select the fault equipment type identification method is the key job in finite reference mode database.

    Intelligent Diagnosis Algorithm of Power Equipment based on Acoustic Signal Processing

    Shutao Zhao, Baoshu Li, Yumin Ge and Weiguo Tong



    Third International Workshop on Advanced Computational Intelligence August 25-27, 2010 - Suzhou, Jiangsu, China

    978-1-4244-6337-4/10/$26.00 @2010 IEEE

  • Fig.1. Principle block diagram of the acoustic recognition system


    A. Principle of Dynamic Time Warping Dynamic programming is utilized in solution a global optimizing problem, the DTW convert the optimizing procedure to several partial optimizing problem, and decision would be made step by step, as in [5],[6]. Given a saved template and the matching series of one acoustic signal series A, and B, of lengths {A} and {B},

    { }1 2, , , iA a a a= { }1 2, , , jB b b b=

    The DTW algorithm is to derive a best time merge function that minimizes skewness from acoustic signal time axis i to match board time axis j.

    Construct a warp path C

    ( ) ( ) ( ){ }1 , 2 , ,C c c c N= (1) max( , )i j N i j < +

    where N is the length of the warp path and the nth element of the warp path is

    ( ) ( ) ( )( ),c n i n j n= (2) where i, j are an index from time series A, and B. Every index of both time series is needed to be used in the warp path and need to increase monotonically in the warp path.

    ( ) ( ) ( )( )1 1 , 11, 1

    c n i n j n

    i i i j j j

    + = + + < + < +


    The minimum-distance warp path is optimal, where the

    distance of a warp path D is as (4).

    ( ) ( )( )1


    ,m in


    ni n j nn



    d a a WD






    Dist(D) is the distance of warp path D , and Dist(wki, wkj) is

    the distance between the two data point indexes in the kth element of the warp path.

    A two-dimensional {A} by {B} cost matrix D, is constructed where the value at ( ) ( )( ),i jd a n b n is the minimum distance warp path that can be constructed from the two time series ( )ia n and ( )jb n . The value at D will contain the minimum-distance warp path between time series ( )ia n and ( )jb n . The i-xis is the time of time series A, and the j-axis is the time of time series B.

    B. DTW Feature Extracting and Type Matching The DTW algorithm destination is to calculate the

    similarity ratio between test acoustic signal and match board frame, and performing the least distance vector by search the optimally warp path essentially. In the calculation process, the feature selection and vector configuration is significant to make the pattern recognition, as in [7], [8].

    In laboratory test, 200 acoustic signal segments have been acquired from power transformer, potential transformer, current transformer and oil breaker. These signal is divided into 50 cluster, and each one include 10 second separately. MFCC coefficient is adopted as the matching feature, as shown in Fig.2.

    Fig. 2. The MFCC coefficient calculation flow

    The difference of acoustic signal frame is considered very small when power equipment normal running in a short-time as in [9],[10]. The MFCC coefficient is obtained form the following steps.

    (1)The acoustic framing signal pass through the Hamming Windowthe spectrum is calculated by short-time FFT. The signal is steady in 10 second, and all the sampling points are utilized.

    (2) Spectrum square obtained, it is the energy spectrum, with K Mel filter band-pass filter.

    (3) The output of each filter is implemented Logarithmic transmission to obtain the log power spectrum corresponding to the frequency bands. Anti-discrete cosine transform is done to obtain MFCC coefficient, as shown in


  • 1


    ( 0.5)( ) ( )K


    i PMFCC Y i COSK


    = (5) where, ( )Y i is the i-Mel filter on the number of energy output; P express MFCC parameters of the order; K is Mel filter banks in the number of triangular filters.

    In the matching process, the template is the acoustic signal that have been acquired previusly of the four kind of power equipments in normal running state. And via transforrned like Fig. 2 process, MFCC coefficient is calculated. This is the match borad as standad series A, and test signal MFCC coefficient ( )Y i as the input series B.

    C. DTW Match Board Obtained Method Assume that only consider the single acoustic signal of

    power equipment. Define { }11 11 12 1, ,..., TX x x x= as the firs group of signal feature vector sequence,

    { }22 21 22 2

    , ,..., TX x x x= as the second groups of feature vector sequence. DTW algorithm through the distortion of the two templates scores ( )1 2,d X X , if ( )1 2,d X X less than a certain threshold , that consider the two feature vector sequence is better, then the average sum of 1X and 2X time warping are calculated and a new template

    { }1 2, ,..., TyY y y y= is obtained. yT is the DTW algorithm that the optimal path length, and

    the optimal path sequence can be get finally.

    ( ) ( )( ) ( ) ( )( ) ( ) ( )( )1 , 1 , 2 , 2 , ..., ,y yT y T yi j i j i T j T 6

    The new template can be calculated by:

    ( ) ( )( )1 21 , , 1, 2, ...,2k yi k j ky x x k T= = 7

    However, the band-pass filter contained in the number of K and the MFCC filter order of P need to determine the optimal value by experiments. DTW method is applied in electric equipment sound source identification, and we find that increasing the number of K filter when the MFCC order of P remains the same, the recognition rate and no substantial increase. But When the filter is the number of K remains unchanged, increasing the number of P-order MFCC, the recognition rate was improved, and in this experiment, select P = 10, K = 14 is the best. DTW algorithm is applied acoustic signal sound source identification rate of 82.6%. The recognition results as shown in the Table I.

    In this paper, through MFCC coefficients as the feature matching pre-made templates, DTW similarity calculation method is utilized to identify the power equipment type firstly, and via the type check equipment operation information according to the historical data and expert knowledge, and then finally local energy band based wavelet packet decomposition method is utilized to judge the state of

    equipment in a normal or abnormal, and determine the severity of further failure.



    K 5 6 7 8 9 10 11 12

    10 67.8% 68.1% 68.4% 68.7% 69.6% 79.8% 69.2% 69.3%

    12 68.3% 69.7% 69.7% 69.6% 72.3% 82.1% 72.4% 71.4%

    14 68.7% 70.7% 70.9% 70.5% 72.7% 82.6% 72.1% 72.5%


    A. Acoustic Signal Non-stationary and Conventional Wavelet Analysis

    Acoustic signal is always relatively smooth, localized, rather than stationary is absolute and the overall situation. When the power equipments in abnormal state, the equipment running acoustic signal was generally non-stationary, that this time the signal is time-varying non-stationary. Feature extraction is to extract the signal from the state of fault-related features and equipment information (symptom). It runs through the entire fault detection and isolation process. Signal amplitude and frequency components will change over time, and the wavelet packet is commonly used method to the feature extraction.

    The N length signal is by decompose J level wavelet packet, and JN wavelet packet coefficients can be derived, as in [11]-[13]. Even using the optimal basis selection, the decomposition coefficient is N at least. In particular, the length of the signal is long, from the feature dimension is considerable. According to transformers acoustic signal frequency distribution after Fourier transform, the energy of acoustic signals in transformer is usually concentrated in the high frequency part. The traditional wavelet packet algorithm to the entire frequency range of attainment for feature extraction, can not distinguish clearly between the various conditions characteristic, hard to improve the analysis precision and accuracy.

    B. Feature Extraction from Wavelet Packet Decomposition A new method that sections wavelet packet decomposition

    based on frequency local energy was used in feature extraction, and it can decompose each frequency band according to requirement.

    The characteristic information in some frequency band can be obtained after signal decomposed by wavelet packet, it means that the original signal was decomposed on 2N orthogonal frequency bands, as in [14]-[17]. The energy summation on each frequency band is agreement with original signal, and the signal component on frequency band characterized original information in each range. The sections wavelet packet decomposition based on frequency local energy is defined as

    ( )22





    NE =






    ij ddttS (8)


  • where, SNj is characteristic signal composed by coefficients of each frequency band from low to high on Nth layer, i is time quantum of SNj, t1 and t2 were the beginning and ending time corresponding to the time quantum i, M1 and M2 were the subscripts of discrete points corresponding to t1 and t2, ,j nld is coefficient of the lth discrete point.

    The local energy band based wavelet packet decomposition method is applied in feature extraction, the low frequency is

    not further broken down, and the main part for the high-frequency decomposition, through the decomposition of the signal is still 8.

    Five groups of test data were used. Table is an example of that eigenvector calculates results of transform internal poor contact.

    C. Storage and Representation of Knowledge Knowledge of electrical equipment fault is the symbol of

    knowledge and formalized process. The collection knowledge would convert to the internal code form of a computer with appropriate accurate description of the data and storage structure. Common logical knowledge represent methods include logic, semantic networks, rules and instance, etc., this system uses the instance case of knowledge.

    Transformer fault diagnosis as an example, according fault feature that is local energy band wavelet packet decomposition coefficient to determine failure mode firstly, then according failure mode query, last failure reasons and fault changing regular and fault diagnosis strategy can be achieved. The specific diagnostic rules used as in Table recording.



    No. Rule Name Rule Premise Conclusion








    0.3142S7 0.3208

    S7 changed greatly

    compared with the

    normal state











    0.1731S8 0.1801

    0.16S6 ,S7,S80.2





    C1 abnormal




    0.1512S8 0.1827


    operation 0.90

    Knowledge database of fault diagnosis system consists

    of failure mode, fault reason and fault typical, further includes the consequences of failure and maintenance measures. There are many kinds of transformer failure, and failure severity can be featured and characterized through the wavelet packet decomposition calculation. And this can be stored in the computer similar as table . In a relational database environment for knowledge items to add, delete, modify and inspection functions, and maintain the integrity of the knowledge base, consistency and safety.

    D. Fault Diagnosis Module Through experienced field staff of the exchange, at the

    conclusion based on the usual external manifestation of the varies transformer faults, distributed, hierarchical feature expert system knowledge would be established. Knowledge of the table contains the main fields are: fault type, fault model, fault components, fault symptoms and tolerance range.

    Based on the feature of extract from test signal and the expert knowledge, and with the sign library to match the existing signs, determine for the main running state facts. The Expert system diagnostic the fault inference flowchart is shown in Fig. 3.

    According to transformers acoustic signal analysis scheme proposed in this paper, via site of the measuring point selection, the determination of the sound field, then the band energy feature and DTW have been utilized to determine equipment type. Finally, based the fault feature extraction based wavelet packet decomposition, the expert system can be utilized to diagnosis the transformer pre-set failure and its type and severity. Experiment results show that the measured acoustic signal analysis, based on local energy range band wavelet packet feature effectively achieve the diagnosis of transformer condition.


    Groups NO. Locally based band wavelet packet energy feature extraction

    1 0 . 9 2 1 1 0 . 8 3 7 5 0 . 3 1 9 9 0 . 4 8 3 8 0 . 1 5 2 7 0 . 0 8 2 3 0 . 3 1 4 2 0 . 1 6 8 7 2 0 . 9 1 7 3 0 . 8 3 3 6 0 . 3 2 3 3 0 . 4 8 9 1 0 . 1 5 3 9 0 . 0 8 8 7 0 . 3 2 0 8 0 . 1 6 7 7 3 0 . 9 2 2 5 0 . 8 4 0 1 0 . 3 1 6 9 0 . 4 8 8 6 0 . 1 5 6 5 0 . 0 8 6 9 0 . 3 1 9 3 0 . 1 6 2 9 4 0 . 9 2 9 6 0 . 8 3 6 6 0 . 3 1 7 7 0 . 4 7 6 9 0 . 1 4 7 9 0 . 0 8 0 5 0 . 3 1 6 7 0 . 1 6 5 3 5 0 . 9 2 8 3 0 . 8 3 9 2 0 . 3 1 4 8 0 . 4 8 2 5 0 . 1 5 8 3 0 . 0 8 3 4 0 . 3 1 5 8 0 . 1 6 4 8


  • Start

    Signal analysis

    Get signs fact

    Man-machine dialogue?

    Add the fact signs of human-computer dialogue

    Forward reasoning for the candidate fault set

    Backward reasoning on the candidate fault set

    Determine the fault name

    Explain the failure causes

    Troubleshooting recommendations put forward

    Output fault code and suggestions



    Fig.3. Expert system diagnostic inference flowchart

    V. CONCLUSION Using non-contact sensor to collect run-time power

    equipment, selecting appropriate acoustic signal analysis algorithm to determine operational status and fault type, and that ensure stable and reliable operation has important practical significance.

    Through calculating the tested acoustic signal and template signal and vector similarity distance, DTW determined the best path. Utilizing the acoustic signal analyses, it is an effective way to recognize the fault type of power equipment. Based on band of local energy wavelet packet acoustic signal feature extraction, combined with expert system can distinguish fault symptoms, and this greatly increases the accuracy of fault identification.

    Therefore, the transformer state monitoring and fault diagnosis by means of acoustic signals to the expert diagnosis system for the platform has broad application prospects.

    REFERENCES [1] Cipriano Vartoletti, Maurizio Desiderio, Danilo Di Carlo,

    Vibro-acoustic techniques to diagnoses power transformers, IEEE Transaction on Power Delivery, vol. 19, pp. 221-229, Junuary 2004.

    [2] Xu Jing, Wang Jing, Gao Feng. A survey of condition based maintenance technology for electric power equipments, Power System Technology, vol. 24, pp. 48-52, April 2004.

    [3] Liang Xidong, Dai Jianjun, Zhou Yuan-xiang, Ultrasonic detecion on crack of FRP ROD in brittle fracture of composite insulator, in Proc. 25th CSEE, 2005, pp. 110-114.

    [4] Hao Yanpeng, Xie Hengkun, Acoustic diagnostic techniques of electrical insulation for HV power equipment, Advanced Technology of Electrical Engineering and Energy, vol. 22, pp.51-54, February 2003.

    [5] Zhu Jianjun,Cui SHaoping, Zhao Lei, The application of infrared diagnosis technology in diagnosis of The high voltage electrical equipment internal defec, High Voltage Engineering, vol. 30, pp. 34-36, July 2004.

    [6] Okada, S., Hasegawa, O., Ition based on dynamic-time warping method with self-organizing pattern recognition, in Proc. 19th ICPR Conf., 2008,pp. 1-4.

    [7] Berger, A., Della Pietra, S., Della Pietra, V., A maximum entropy approach to natural language, in Proc. Comput. Linguist. 1996,pp. 3971.

    [8] Pedro F. Felzenszwalb, Daniel P. Huttenlocher. Pictorial structures for object recognition, Journal of Computer Vision, 2005.

    [9] Liu, Y., Shriberg, E., Stolcke, A., Hillard, D., Ostendorf, M., Harper, M., Enriching speech recognition with automatic detection of sentence boundaries and disfluencies, IEEE Trans. Audio Speech Lang. Process. Vol. 14,pp.15261540, May 2006.

    [10] Wang, S., Schuurmans, D., Peng, F., Zhao, Y., Learning models with the regularized latent maximum entropy principle, Trans. Neural Networks, vol. 15, pp. 903916,April 2004.

    [11] Pengju Kang, David Birtwhistle, Condition assessment of power transformer on-load tap changers using wavelet analysis and self-or-ganizing map: field evaluation,. IEEE Transaction on Power Delivery, vol. 18, pp.78-84, Juanuary 2003.

    [12] Yan Qiurong, Liu Xin, Yin Jianguo, Features of vibration signal of power transformer using the wavelet theory, High Voltage Engineering, vol. 33 pp.165-168, January 2007.

    [13] I.N.TanselC.Mkdeci, and C.Mclaughlin, Detection of tool failure in end milling with wavelet transformations and neural networks (WT-NN), Int'I J.Machine Tools and Manufacture, vol. 35, pp. 1137-1147, August 1995.

    [14] K. Riley, A. J. Devaney, Wavelet processing of images for target detection, International Journal of Imaging sys. & Technology, pp.404-420, 1996.

    [15] S. Wen, Y. JiangpingL. Jian, .A small IR target detection approach based on multi-scale relative distance image, in Proc. of SPIE, 2000, pp. 194-197.

    [16] Yu Zhiwei, Su Baoku, Zeng Ming, Application of wavelet packet in fault diagnosis system of large scale DC motor rotor, in Proc. 25th of the CSEE, 2005, pp.158-162

    [17] Zhang Jianguo, Sun Xiaodong, Zhang Liyong, Research on signal characteristic extraction method based on the time-frequency analysis, Electrical Measurement & Instrumentation,vol. 42, pp. 6-9, June 2005.


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