Selection of Wavelet for De-noising PD waveforms for ...

2008 Annual Report Conference on Electrical Insulation Dielectric Phenomena

Selection of Wavelet for De-noising PD waveforms for Prognostics and Diagnostics of Aircraft Wiring *Chintan Desai1, Dr.Keith Brown1, Prof. Marc Desmulliez1, and Dr. Alistair Sutherland2 1

Heriot-Watt University - School of EPS, Riccarton, Edinburgh, EH14 4AS, United Kingdom 2 Ultra electronics-BCF, Gloucestershire, GL7 1QG, United Kingdom kilometres of wiring buried deep within their structures begin to crack and fray[1, 2, 4]. A. Arcing Arcing is major cause for catastrophic failure in degradable wiring. Kapton (polyimide) insulation seemed to be dream wire insulation for commercial and military fleets in 1970 and `80s because of extremely light, tough, flame resistant, good dielectric properties and improved heat dissipation. Kapton the aromatic polyimide dries out with age, goes hard and then develops hairline cracks which allow the ingress of water and other aviation fluids. Leakage current flows through this moist layer and creates slight heating then a voltage appears across this dry band originating a small flashover. Micro arcs have a temperature around 10000C which will pyrolize the insulation and form the carbon char leading to the insulation becoming conductive and forming more carbon along the track with time known as wet arc tracking. Once initiated the carbon track becomes self propagating and ends up with “power arc” that can damage a whole wire-bundle[5]. Carbon arc tracking occurs in dry conditions when one or more conductors are shorted due to abrasion from aircraft structure, wire to wire abrasion or maintenance error. NASA reported one of the reason for space shuttle STS93 accident was that a main engine controller wire had arced with an adjacent bolt head [6].

AbstractElectrical wiring and interconnected systems (EWIS) have become a major area of research and development for variety of industries. A variety of factors lead to catastrophic failure described. Here partial discharge (PD) analysis methods for diagnosing aircraft wiring faults are explored since PD signals are strongly correlated with the defects that produce them and form an ideal part for prognostic and diagnostic test system. A simulation of PD signal based on high-voltage insulation testing standard is detailed. Wavelet based (Time-Frequency) analysis is shown to be a good approach to de-noise PD signals. Leading to an overall set of conclusions and on-going work.

I. INTRODUCTION Wiring is a critical system in aircraft, ships, homes, industry, automobiles etc. [1]. Electrical wiring and interconnected systems (EWIS) have become a major area of research and development for variety of industries. Aircraft wiring integrity and safety related issues are known to be very serious and have received a great deal interest after the Swissair 111 and TWA 800 accidents [2]. In both incidents degradation of wiring insulation played a major role. Wiring as small as 28 gauge about 0.33 mm thick is common in aircraft. This thin insulation deteriorates with age due to multifactor stress and lead to catastrophic failure described in section II. Exploring partial discharge (PD) analysis methods for diagnosing aircraft wiring faults is a significant new research direction because PD signals are strongly correlated with the defects that produce it and form an ideal part for prognostic and diagnostic test system. Section III describe traditional and future on-line sensor based PDA (partial discharge analysis) for fault diagnosis. Section IV details a simulation of PD signal based on high-voltage insulation testing standard [3]. Partial discharges are low-voltage high frequency signals buried in noise. Wavelet based (TimeFrequency) analysis is a proven tool to de-noise PD signals. Section V describes an approach to wavelet based PD denoising. Conclusions and on-going work are described in section VI.

III. TRADITIONAL VS PDA (PARTIAL DISCHARGE ANALYSES) TECHNIQUE

The traditional fault detection techniques of aircraft wiring are visual inspection, several versions of reflectometry like TDR (Time domain reflectrometry), FDR (Frequency domain reflectrometry), NDR (noise domain reflectrometry), impedance matching and traditional thermal circuit breakers (CB) [7]. Reflectometry techniques analyse signal produced by reflections from pulses, of predefined characteristics, transmitted through a wire under test due to discontinuities or impedance deviation in the system. Another approach involves measuring the cable’s resistance from end to end. High voltages can be used to detect current leakage between adjacent wires but may itself damage the insulation. Aircraft circuit breakers have historically been the best available protection for aircraft wiring but today’s design are 40 years old [8]. Arcs are spurious by nature and instantaneous energy release levels (80 to 500 Joules) present during an arcing event can be extremely high, the RMS current level over time is far below the trip threshold of a CB[8, 9]. No system is available today that can detect and locate wire anomaly without disconnecting or passing high voltage through the wires[10].

II. FAILURE MODE OF AIRCRAFT ELECTRICAL WIRING AND INTERCONNECT SYSTEM A healthy aircraft wire typically, a copper conductor (from 1 to 10 mm in diameter) is covered by thin outer insulation (from 0.5 to 2 mm thick) and is one of the simplest elements in an electrical system [2]. Planes over 20 years old (average age of civilian aircraft in use is 18 years and military plane is 16 years) are virtually guaranteed to have wiring problems, many of which turn up during routine maintenance. As today’s military and commercial aircraft age past their teen years, the many

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Discharges that do not bridge the electrodes are known as partial discharge. PDA (Partial Discharge Analysis) is a proven diagnostic tool, though not previously used for aircraft wiring that enables identification of initiation stages of the wiring degradation well before insulation failure and development of arc based fires. The electric strength of a dielectric voids in form of crack and other delamiantion comprises gas is lower and stress is higher than that of solid dielectric. Accordingly, when applied voltage is increased to a certain value known as the discharge inception voltage, so that the peak value of the electric stress in the cavity is equal to the electric strength of the gas in it and originate partial discharge in the cavity. The discharge will be self-extinguished and recur during successive half cycle of an applied voltage causes ageing of the insulation material that may lead to ultimate failure [11]. Types of PD strongly depend upon failure mechanism are internal, surface, corona, electric trees, floating discharge and contact noise are useful for smart diagnostic system. Techniques are available for detection, location and evaluation of PD in high voltage machines like generators, transformer winding, and bushing. Health Usage and Monitoring System (HUMS) based on a novel kind of PDA method for aircraft wiring with a complete array of MEMS embedded sensors can be installed inside the EWIS system. A combined wavelet based intelligent signal processing technique allows the sensing of small changes in the wiring dielectric properties, to discriminate between ordinary transient signatures and to remove background noise. The partial discharge signal is detected by low cost high frequency rogowski coil based passive sensing method. Sensor informatics with intelligent signal processing for diagnosis and prognostic of EWIS can be implemented for predictive online CBM (conditional based monitoring) instead of time based maintenance in future aircraft where PD analysis is key technology to measure insulation integrity.

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Fig.3. SIMULINK model and response for narrow-band PD detector where τ = 2μs, ω0 = 270 KHz

V. DISCRETE WAVELET TRANSFORM FOR PD DE-NOISING Partial discharge analysis requires sophisticated digital signal processing techniques because in aircraft systems discharges hampered by various noise sources. Traditional techniques used for signal processing are realized either in time or frequency domain. PD pulses are always non-periodic and fast transient, irregular features and are impossible to detect using traditional frequency based transforms. MRA (multi resolution) based wavelet analysis may be used to detect abrupt changes in a signal and ability to perform local analysis with time-scale approach. The power of Wavelet transform for denoising has been well documented [12]. Ψ(t) is selected as mother wavelet, the family of scaled and time –shifted version of Ψ(t) can be described as

ψ a (t ) =

1 ⎛t −b⎞ + ψ⎜ ⎟ , a ∈ R − {0}, b ∈ R a ⎝ a ⎠

(1)

Where variable a=2j is scaling factor at j level and b=ka is translation of wavelet [13]. Several families of wavelets already proven to be useful in application of PD de-noising are time, frequency or orthogonal filter based [14]. Equation (1) states that Ψ(at) is inversely proportional to a-1/2; whereas its duration (t) is linearly proportional to a. Scale of wavelet can correlate with different centre frequency which captures the oscillation. Spectrum defines low values of a higher the frequency and larger Δƒ, where as high values of a lower the frequency and lower Δƒ. Wavelet transform useful to differentiate global features (low-frequency) and detail (high-frequency) involved in a signal and can study simultaneously [13, 15].

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A PD detector response was simulated. Figure (1) shows typical partial discharge detection system where Ca is the test specimen which integrity required to measure, Ck is coupling capacitor for stabilising voltage and fast charge transfer and RC-RLC based coupling impedance which quantifies discharge by integrating the current pulse flowing through it. Figure (2), (3) shows SIMULINK® modelling of RC and RLC based detector using their transfer function in Laplace form. Actual PD current pulse has finite duration but for modelling purposes a Dirac current pulse i(t) is used. Output voltage pulse V (t) is represented by damped exponential pulse in RC impedance circuit and damped oscillatory in RLC impedance circuit.

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IV. SIMULATION OF PARTIAL DISCHARGE SIGNAL

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A. Optimal Selection of Wavelet for PD De-noising Partial discharge de-noising may be divided into a step-bystep process. Selection of the wavelet is key step because it captures signal oscillation buried in noise. Narrow band RLC-

Equivalent Ckt of Cable, Ca=Cable capacitor, Detector Coupling Impedance Cv=Internal Void capacitor, Cp in series with Cv

Fig.1. High Voltage Partial discharge detection circuit

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DOP (damped oscillatory pulse) simulated using data in reference [14] where the amplitude is normalized, τ (damping factor=5.0μs), ƒ0 (resonant frequency= 260 K Hz), T (sample time= 0.25 ms) and sampling rate is ƒs=10 MHz. A PD signal is transient in nature and DWT decomposes an approximation and detail coefficients with high and low pass QMF filter. The approximation keeps low frequency content and detail extracting transient features between successive approximation levels. 3-D plot of detail coefficients at various levels shows the level particular wavelet correlate with the signal by stretching and compressing with time[15]. When the shape of the wavelet matches the signal it will maximizes the coefficient at particular level. Figure (4) shows 3-D plot of detail coefficient for 7 level of decomposition of signal using db7, db10. The result shows that db10 is the best wavelet for simulated PD signal because it increases the local maxima in fewer levels. Decomposition using db7 wavelet

Simulated Noisy PD signal

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7 levels of decomposition using signal length and entropy level were calculated using MATLAB wmaxlev function [13]. Use of more levels for decomposition is redundant. Figure (7), shows seven levels of decomposition using db10 and DWT scheme for the simulated noisy PD signal. The wavelet decomposition of the signal in time-scale view shows most of the noise concentrated in detail at lower level. It can be seen that QMF of db10 splits the frequency band in high and low frequency component.

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composition be no less than the length of the wavelet filter being used [13, 14].

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Fig.4. 3-D pattern of wavelet coefficients of PD signal using ‘db7’ (left) and ‘db10’ (right)

B. Noise Simulation and Wavelet-Decomposition Sources of noise are various on-board systems and other external sources. Communication radio signals are taken into account here and simulated using value and data from reference [14]. Stochastic noise, random both in amplitude and time associated with amplifier, other electronics equipment is widely spread through out spectrum and hard to remove. In figure (5), 3-D pattern of decomposed detail coefficients using db10 shows that noise related coefficients are widely spread and concentrated at different level than the PD signal, they also vary with different type of noise.

Fig.7. Noisy PD signal seven level decomposition using ‘db10’ wavelet

D. Automatic Thresholding and Reconstruction Thresholding is another key step for de-noising which involves removing noise related coefficient and select PD related. Eliminating levels with little PD signal energy content prior to reconstructing give better result with little loss of signal in process of feature extraction and classification. The “soft” and “hard” thresholding are techniques where the former removes the coefficient whose value is lower than threshold number and in addition compresses the remaining one where latter one just removes the coefficient whose value lower than threshold. This work agrees with result found in [12] that hard threshold removes the noise with little loss of signal compare to soft threshold. Threshold estimators are Stein’s unbiased risk estimate (SURE), Fixed form, Minimax, Combination of SURE and fixed form are well estimate level of general Gaus-

Decomposition using db10 wavelet

Decomposition using db10 wavelet

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C. Simulated noisy PD signal and DWT decomposition Figure (6), shows simulated PD signal combined with noise mentioned above. The SNR, is -9.8257 dB which is quite high. It is required that the signal length at the highest level of de-

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sian white noise[13]. If N is the noise level in signal s than global threshold selection rule using fixed form estimator is

high voltage narrow-band and wide-band detector response simulated has been shown. The 3-D plot of detailed wavelet coefficients measured maximum correlation between signal and wavelet at various scales. The db10 proved to be the best for simulated narrow-band PD signal. The noise and PD related wavelet coefficients are concentrated at different scale so noise can be easily removed. The level based threshold of the wavelet coefficient as per equation (2) is an effective for simulated noisy PD signal which has SNR = -9.8257 dB. Simulation results effectiveness, mean square error (MSE) for de-noise signal is 0.0017 (only thresholding) and 0.0016 (removing low frequency coefficients). Future work involves the simulation of PD signal using wide-band HFRG (high frequency rogowski) current sensor, and an experimental set-up to confirm the concept of detecting PD signal from low level of voltage (115 VAC,400 Hz) applied to aircraft wire works.

N 2 * log(length( s ) [16]. PD signal buried in various types of non white and unscaled noise so ignore the global noise level it must be estimated. The median absolute deviation of all coefficients c at level j is use to rescaled the threshold known as level dependent threshold λj [13]. (2) λ j = MAD (Cj ) / 0 .6745 * 2 * log( length ( s ) References [14, 15] mentioned threshold selection using level dependent coefficient length (nj) but stimulated signal has high SNR (-9.825 dB) ratio so total signal length better estimate noise level. Because as noise level is high using total signal length calculate threshold comparable to noise level and easily remove noise related coefficient. Using wavelet admissibility condition threshold wavelet coefficient reconstructed using IDWT (inverse discrete wavelet transform).Reconstructed denoise signal= IDWT (threshold (Approximation of level 7+ Detail of level 1 to 6)). Figure (8) shows de-noised signal and its frequency spectrum.

REFERENCES [1]

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The frequency spectrum shows the de-noised signal containing low-frequency noise. Computing using frequency sub-band method for db10 wavelet for sampling frequency (Fs=10 MHz), maximum can be seen is fs/2, cd5 (detail wavelet coefficient at level 5) contain highest signal energy [14]. Method highly depends upon frequency band because it changes with sampling frequency. Removing high level of wavelet coefficients cd6, cd7, ca7 gives much ‘cleaner’ frequency spectrum of de-noise signal as shown in figure (9). 1

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VI. CONCLUSION AND FUTURE WORK

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PD diagnosis has been successfully applied to detect the integrity of high voltage insulation systems. Proof of concept for PD based on-board aircraft wiring diagnosis method based

[16]

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