Cepstrum Analysis Research Articles

Determination of the State of Wear of High Contact Ratio Gear Sets by Means of Spectrum and Cepstrum Analysis

The paper presents basic procedures and methods used for determining the state of wear of high contact ratio (HCR) gear sets comparing frequency spectra and cepstra recorded throughout their lifetime tests, through vibro-acoustic diagnostics. After a study and calculation of characteristic frequencies, the authors experimentally measured the dynamical behavior of the gear sets in order to determine their frequency spectra. To gain additional information about possible damage, cepstrum was evaluated for each measurement as well. Experiments were carried out on a FZG back-to-back test gearbox, equipped with HCR test gears during their lifecycle. The frequency spectrum and cepstrum was assigned to a specific percentage of pitting occurrence. Analyzed values of amplitudes at mesh frequency components and their sidebands (as well as corresponding quefrencies) in the spectrum (and cepstrum) were compared and the state of wear was assigned to each frequency (quefrency) response. The results from lifetime tests of two gear sets indicate that by means of FFT (as well as cepstrum) analysis the incubational phase (i.e., a cavity origination under a surface in contact) of the gear fault can be determined. This is not possible using classical (e.g., visual) methods. Furthermore, it was observed that pitting and thermal scuffing are distinguishable also at higher harmonics (the fifth and even sixth), which can be also used as an indicator of gear damage. Vibro-acoustic diagnostics is a feasible nondisassembling method used for the investigation and prediction of gear failure level in HCR gear wheels. It is shown to be a reliable method capable of predicting failure earlier than a classical visual (disassembling) approach.

A comparative study on vibration‐based condition monitoring algorithms for wind turbine drive trains

ABSTRACTThe ability to detect and diagnose incipient gear and bearing degradation can offer substantial improvements in reliability and availability of the wind turbine asset. Considering the motivation for improved reliability of the wind turbine drive train, numerous research efforts have been conducted using a vast array of vibration‐based algorithms. Despite these efforts, the techniques are often evaluated on smaller‐scale test‐beds, and existing studies do not provide a detailed comparison between the various vibration‐based condition monitoring algorithms. This study evaluates a multitude of methods, including frequency domain and cepstrum analysis, time synchronous averaging narrowband and residual methods, bearing envelope analysis and spectral kurtosis‐based methods. A full‐scale baseline wind turbine drive train and a drive train with several gear and bearing failures are tested at the National Renewable Energy Laboratory (NREL) dynamometer test cell during the NREL Gear Reliability Collaborative Round Robin study. A tabular set of results is presented to highlight the ability of each algorithm to accurately detect the bearing and gear wheel component health. The results highlight that the cepstrum and the narrowband phase modulation signal were effective methods for diagnosing gear tooth problems, whereas bearing envelope analysis could confidently detect most of the bearing‐related failures. Copyright © 2013 John Wiley & Sons, Ltd.

Assessment of hypernasality for children with cleft palate based on cepstrum analysis

Hypernasality is a frequently occurring resonance disorder in children with cleft palate. In general, an operation is necessary to reduce the hypernasality and therefore an assessment of hypernasality is imperative to quantify the effect of the surgery and design the speech therapy sessions, which are crucial after surgery. In this paper, a new quantitative method is proposed to estimate hypernasality. The proposed method used the fact that an autoregressive (AR) model for vocal tract system of a patient with hypernasal speech is not accurate; because of the zeros appear in the frequency response of the vocal tract system. Therefore in our method, hypernasality was estimated by a quantity calculated from comparing the distance between the sequences of cepstrum coefficients extracted from AR model and autoregressive moving average model. K-means and Bayes theorem were utilized to classify the utterances of subjects by means of proposed index. We achieved the accuracy up to 81.12% on utterances and 97.14% on subjects. Since the proposed method needs only computer processing of speech data, compared with other clinical methods it provides a simple evaluation of hypernasality.

PSF estimation via gradient cepstrum analysis for single blurred image

Since single image restoration algorithms using lots of priori information lead to high complexity and low computational efficiency, a gradient cepstrum analysis method is proposed to estimate the point spread function PSF for a single blurred image. Firstly, we present the basic principle of estimating PSF from gradient cepstrum of a single blurred image and use the phase retrieval algorithm to recover phase information of the two-dimensional PSF, which can obtain the estimated PSF rapidly, Secondly, to evaluate the accuracy of the proposed PSF estimation method, the total variation regularized image restoration model coupling with an image gradient fidelity term is established and an alternating direction method with rapid and stable convergence is adopted to optimize the energy function. Both synthetic and real blurred images are tested to verify the performance of our scheme. Results show that our scheme not only can estimate the PSF rapidly and accurately so that it overcomes shortcomings of traditional algorithm with slow convergence, but also suppresses ringing effects to preserve information in edges. These advantages provide theoretical and technical foundation of the real engineering requirement in single image deblurring, especially for large scale images.

Onsite leak location in a pipe network by cepstrum analysis of pressure transients

Leakage is a major source of lost revenue in pipeline distribution networks. In UK water distribution networks, the amount of treated water lost within distribution networks can reach up to 40% of the total supply. Existing leakage location techniques are heavily dependent on acoustic sensing, which can perform poorly in situations in which the noise created by a leak is small or attenuates rapidly. A reliable, nonacoustic‐based location technique would be a significant advance for the water industry. The work presented here applies a pressure transient‐based technique for leak location, which has previously only been tested under laboratory conditions, to a live water distribution network. The method has the potential to increase the speed and accuracy of leak locations and reduce the occurrence of inaccurate leak diagnosis. A single device that fits to standard fire hydrants is used to generate a mild transient and collect the required data. This method is based on analyzing pressure waves reflected by leaks and other features in a pipeline and accurately identifies reflections in measurements of the small pressure wave caused by pipeline features, including leaks. The technique requires accurate estimation of the wave propagation speed; a simple empirical approach for determining this is included.

Characteristic analysis of air pressure wave generated by high-speed trains traveling through a tunnel

It is a complicated dynamic phenomenon when a transient pressure pulse is triggered by two high-speed trains passing each other in a tunnel. The air pressure pulse is a transient excitation to side wall of the car body. It can stimulate almost all vibration modes of the car body and the correlated assemblies, cause serious aerodynamic noise, and have important impacts on car body distortion, train noise, and operation safety. This article analyzes the time-frequency characteristics and main parameters of field-measured the air pressure wave, and its relationship with the train velocity as well as the vibration of the car body. Cepstrum analysis concludes that in the process of the meeting, the air pressure wave in tunnel crossing is a multiplying pressure wave instead of a superposed wave. The pressure pulse during the meeting is non-symmetrical one featured with a sharp front, large amplitude, fluctuating central section, and less sharp tail. The pulse width is inversely proportional to the train speed. As the speed increases, the impulse amplitude is amplified, and the speed of pulse front is advanced.

Vowel- and Text-Based Cepstral Analysis of Chronic Hoarseness

Automatic voice evaluation is usually performed on stable sections of sustained vowels, which often cannot capture hoarseness properly. The measures cepstral peak prominence (CPP) and smoothed CPP (CPPS) do not require exact determination of the cycles of fundamental frequency like established perturbation-based measures. They can also be applied to text recordings. In this study, they were compared with perceptual evaluation of voice quality and the German roughness-breathiness-hoarseness (RBH) scheme. Retrospective data analysis. Seventy-three hoarse patients (48.3±16.8 years) uttered the vowel /e/ and read the German version of the text "The North Wind and the Sun". The text recordings were evaluated perceptually by five speech therapists and physicians according to the RBH scale. The criterion "overall quality" was measured on a 4-point scale and a visual analog scale. For the human-machine correlation, the automatic measures of the Praat program (vowels only) and the "cpps" software were compared with the experts' ratings. The experiments were repeated for speakers with jitter ≤5% or shimmer ≤5% (n=47). For the entire group (n=73), the best human-machine results for most of the rating criteria were obtained for text-based CPP and CPPS (up to |ρ|=0.73). For the 47 selected speakers, the correlation was remarkably worse for all measures but still best for text-based CPP and CPPS (|ρ|≤0.50). Cepstrum analysis should be performed on a text recording. Then, it outperforms all perturbation-based measures, and it can be a meaningful objective support for perceptual analysis.

Integrated Analysis for Gearbox Fault Based on Autocorrelation and Spectrum Analysis

The advantages and disadvantages of traditional gearbox fault diagnosis method based on autocorrelation, spectrum-zoom, Hilbert demodulation and cepstrum analysis are respectively analyzed. Then a gearbox fault diagnosis method is presented, which integrates autocorrelation, spectrum-zoom, Hilbert demodulation and cepstrum analysis. Finally, vibration data of the pitting gear is measured by using QPZZ-II rotating-machinery fault simulating system. The result shows that the improved method proposed in this paper is effective for the fault diagnosis of the pitting gear.This template explains and demonstrates how to prepare your camera-ready paper for Trans Tech Publications.. The best is to read these instructions and follow the outline of this text. Please make the page settings of your word processor to A4 format (21 x 29,7 cm or 8 x 11 inches); with the margins: bottom 1.5 cm (0.59 in) and top 2.5 cm (0.98 in), right/left margins must be 2 cm (0.78 in). We shall be able to publish your paper in electronic form on our web page http://www.scientific.net, if the paper format and the margins are correct. Your manuscript will be reduced by approximately 20% by the publisher. Please keep this in mind when designing your figures and tables etc.

Evaluation of Food Freshness and Locality by Odor Sensor

The aim of this study was to investigate whether food freshness and locality can be classified using a food evaluation system consisting four SnO2-semiconductor gas sensors and a solid phase column, into which collecting aroma materials. The temperature of sensors was periodically changed to be in unsteady state and thus, the sensor information was increased. The parameters (in quefrency band) were extracted from sensor information using cepstrum analysis that enable to separate superimposed information on sinusoidal wave. The quefrency was used as parameters for principal component and discriminant analyses (PCA and DCA) to detect food freshness and food localities. We used three kinds of strawberries, people can perceive its odors, passed from one to three days after harvest, and kelps and Ceylon tea, people are hardly to perceive its odor, corrected from five areas as sample. Then, the deterioration of strawberries and localities of kelps and Ceylon teas were visually evaluated using the numerical analyses. While the deteriorations were classified using PCA or DCA, the localities were classified only by DCA. The findings indicate that, although odorant intensity influenced the method detecting food quality, the quefrency obtained from odorant information using cepstrum analysis were available to detect the difference in the freshness and the localities of foods.

Data-Based Fault Diagnosis of Power Cable System: Comparative Study of k-NN, ANN, Random Forest, and CART

AbstractGround faults are major problems of power cable systems. They are caused by various accidents and need to be fixed as early as possible. Therefore, it is crucial to diagnose a ground fault. Time-series data of voltage and current when a ground fault occurs is available for diagnosis. In the present work, a data-based fault diagnosis system of power cable systems is developed. In order to achieve the high fault diagnosis performance, new feature variables are generated by using both wavelet analysis and cepstrum analysis. In addition, six classification techniques, i.e. k-nearest neighbor (k-NN), artificial neural network (ANN), boosted ANN (B-ANN), random forest (RF), classification and regression trees (CART) and boosted CART (B-CART), are compared. Furthermore, B-ANN and B-CART are combined with the naive Bayes classifier to cope with multiclass problems. The results of applying the proposed methods to real ground fault data show that B-ANN with the naive Bayes classifier can achieve the best diagnosis performance, which satisfies the requirement for its industrial application.

Emerging Challenges and Technologies in Signal Processing for Prognostics and Health Management in Wind Energy

The use of condition monitoring (CM) in wind energy machines continues to evolve as wind energy machines grow in size and move offshore. Early and smaller wind generation machines offered little financial incentives for condition monitoring, justifying only simple and inexpensive health monitoring technologies. Today, multi-megawatt wind machines are more complex, more difficult to physically reach, and generate more revenue than previous models. This paper reviews challenges and candidate technologies for next generation condition monitoring in Wind Energy.
 Larger wind turbines typically employ Doubly-Fed induction generators with gearbox based drive trains or direct drive generators with multi pole rotors and fixed stators. Both configurations employ variable speed wind driven rotors, variable due to wind speed. Fixed rotor speed signal processing techniques no longer work in a variable speed environment. Synchronous sampling, order analysis, wavelet filters, Cepstrum and related frequency analysis of sensor waveforms are examples of advanced feature extraction tools now available for up-tower condition monitoring systems to address the variable speed nature of modern wind turbines. These signal processing tools operate to reduce and preprocess sensory data producing and extracting signal features. With extracted features, performance prediction and health diagnostics are then able to produce machine degradation rate and degradation levels.
 This paper provides a tutorial of signal processing techniques for analysis of sensory information from variable speed rotary machines. The paper concludes with a discussion of prediction and diagnostics techniques which consume the analysis results of previously mentioned signal processing techniques.

Speech Emotion Analysis: Exploring the Role of Context

Automated analysis of human affective behavior has attracted increasing attention in recent years. With the research shift toward spontaneous behavior, many challenges have come to surface ranging from database collection strategies to the use of new feature sets (e.g., lexical cues apart from prosodic features). Use of contextual information, however, is rarely addressed in the field of affect expression recognition, yet it is evident that affect recognition by human is largely influenced by the context information. Our contribution in this paper is threefold. First, we introduce a novel set of features based on cepstrum analysis of pitch and intensity contours. We evaluate the usefulness of these features on two different databases: Berlin Database of emotional speech (EMO-DB) and locally collected audiovisual database in car settings (CVRRCar-AVDB). The overall recognition accuracy achieved for seven emotions in the EMO-DB database is over 84% and over 87% for three emotion classes in CVRRCar-AVDB. This is based on tenfold stratified cross validation. Second, we introduce the collection of a new audiovisual database in an automobile setting (CVRRCar-AVDB). In this current study, we only use the audio channel of the database. Third, we systematically analyze the effects of different contexts on two different databases. We present context analysis of subject and text based on speaker/text-dependent/-independent analysis on EMO-DB. Furthermore, we perform context analysis based on gender information on EMO-DB and CVRRCar-AVDB. The results based on these analyses are promising.

Vibration response of spalled rolling element bearings: Observations, simulations and signal processing techniques to track the spall size

Fatigue in rolling element bearings, resulting in spalling of the races and/or rolling elements, is the most common cause of bearing failure. The useful life of the bearing may extend considerably beyond the appearance of the first spall and a premature removal of the bearing from service can be very expensive, but on the other hand chances cannot be taken with safety of machines or personnel. Previous studies indicated that there might be two parts to the defect vibration signal of a spalled bearing, the first part being originating from the entry of the rolling element into the fault (de-stress) and the second part being due to the departure of the rolling element from the fault (re-stress). This is investigated in this paper using vibration signatures of seeded faults at different speeds. The acceleration signals resulting from the entry of the rolling element into the spall and exit from it were found to be of different natures. The entry into the fault can be described as a step response, with mainly low frequency content, while the impact excites a much broader frequency impulse response. The latter is the most noticeable and prominent event, especially when examining the high pass filtered response or the enveloped signal. In order to enable a clear separation of the two events, and produce an averaged estimate of the size of the fault, two approaches are proposed to enhance the entry event while keeping the impulse response. The first approach (joint treatment) utilizes pre-whitening to balance the low and high frequency energy, then octave band wavelet analysis to allow selection of the best band (or scale) to balance the two pulses with similar frequency content. In the second approach, a separate treatment is applied to the step and the impulse responses, so that they can be equally represented in the signal. Cepstrum analysis can be used to give an average estimate of the spacing between the entry and impact events, but the latter can also be assessed by an arithmetic estimation of the mean and standard deviation of the event separation for a number of realizations, in particular for the second approach. In order to determine the effects of various simulations and signal processing parameters on the estimated delay times, the entry and exit events were simulated as modified step and impulse responses with precisely known starting times. The simulation was also found useful in pointing to artefacts associated with the cepstrum calculation, which affect even the simulated signals, and have thus prompted modifications of the processing of real signals. The results presented for the two approaches give a reasonable approximation of the measured fault widths (double the spacing between the entry and impact events) under different speed conditions, but the method of separate treatment is somewhat better and is thus recommended.

Research on Cepstrum Analysis of Drill String Vibration and Extraction of Bit Source Signals

AbstractExtracting effective signals from bit in noises from rigs and machines on ground is a key step in SWD (Seismic while drilling) pilot data preprocessing. Source signals from bit, because of the broad range of frequencies and short time duration, can be easily affected by the noises from rigs and machines. Cepstrum analysis is a nonlinear filtering technology, which can change the convoluted signals in time domain into added signals in frequency domain. This method can remove the structural reverberation to extract the source signals by selecting a window function. In this paper, we discuss the cepstral filtering and extract the transient source signals according to the simulation data of drill string. Furthermore, SWD pilot data are processed by using this method in order to realize the intensifying of bit signals.

Gear fault detection and diagnosis under speed-up condition based on order cepstrum and radial basis function neural network

Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. Therefore, most conventional signal processing methods based on time invariant carried out in constant time interval are frequently unable to provide meaningful results. In this paper, a study is presented to apply order cepstrum and radial basis function (RBF) artificial neural network (ANN) for gear fault detection during speedup process. This method combines computed order tracking, cepstrum analysis with ANN. First, the vibration signal during speed-up process of the gearbox is sampled at constant time increments and then is re-sampled at constant angle increments. Second, the re-sampled signals are processed by cepstrum analysis. The order cepstrum with normal, wear and crack fault are processed for feature extracting. In the end, the extracted features are used as inputs to RBF for recognition. The RBF is trained with a subset of the experimental data for known machine conditions. The ANN is tested by using the remaining set of data. The procedure is illustrated with the experimental vibration data of a gearbox. The results show the effectiveness of order cepstrum and RBF in detection and diagnosis of the gear condition.

Intermittent-Chaos-and-Cepstrum-Analysis-Based Early Fault Detection on Shuttle Valve of Hydraulic Tube Tester

To ensure the safety and continuity of production, make a reasonable maintenance plan, and save the cost of maintenance for a hydraulic tube tester, it is needed to quickly identify an assignable cause of a fault. This paper is concerned with early fault detection of shuttle valves, which are the key components in hydraulic tube tester. An intermittent-chaos-and-cepstrum-analysis-based method is proposed to detect this early fault on the hydraulic tube tester. The presented approach is based on the insight that the phase transition of Duffing oscillator is very sensitive to a periodic weak signal having tiny angular frequency difference with the reference signal in this oscillator. Thus, to determine the angular frequency of the reference signal, cepstrum analysis is used to determine the eigenfrequency of the fault signal, and then, the angular frequency of the referential signal is computed according to this eigenfrequency. Finally, a Lyapunov exponent is introduced to realize computer observation of the shuttle valve early fault. The presented method is experimented with data simulated from an AMESim model of hydraulic tube tester. The results indicate that the proposed approach is capable of detecting the signal of shuttle valve early fault in the hydraulic tube tester.

Acoustic analysis of musical intervals in modern Byzantine Chant scales

The goal of this work is to investigate experimentally the music intervals in modern Byzantine Chant performance and to compare the obtained results with the equal temperament scales introduced by the Patriarchal Music Committee (PMC). Current measurements resulted from pressure and electroglottographic recordings of 13 famous chanters singing scales of all the music genera. The scales' microintervals were derived after pitch detection based on autocorrelation, cepstrum, and harmonic product spectrum analysis. The microintervallic differences between the experimental values and the PMC's ones were statistically analyzed indicating large deviation of the mean values and the standard deviations. Significant interaction effects were identified among some genera and between ascending and descending scale directions.

Periodicity of North–South asymmetry of sunspot area revisited: Cepstrum analysis

The North–South asymmetry in solar activity is an important solar property. During recent years the presence of short and intermediate-term periodicities of the North–South asymmetry has been reported, implying it could be an observational constraint that should be explained by the solar dynamo action. We attempt to confirm reported periodicities by employing an independent method so that observational constraints are established. We adopt the cepstrum technique to deconvolve the signal and the noise so that spurious peaks due to random noise are eliminated in the Fourier power spectrum. We also demonstrate how effectively it removes random noise using artificial data generated by assuming that the asymmetry of the sunspot area is characterized by random noise superposed on a slowly varying sinusoidal background function. We find that (i) the main periodicity of the North–South difference corresponding to ∼9 years is present, (ii) other periodicities recently claimed, such as, of ∼1.4, ∼3.8, ∼43 years, can not survive the deconvolution process, suggesting they seem due to stochastic random noise. We conclude by pointing out a possible implication of our finding on the nature of the North–South asymmetry of solar activity.

Cepstrum Analysis of Terrestrial Impact Crater Records

Study of terrestrial impact craters is important not only in the field of the solar system formation and evolution but also of the Galactic astronomy. The terrestrial impact cratering record recently has been examined, providing short- and intermediate-term periodicities, such as, Myrs, Myrs. The existence of such a periodicity has an implication in the Galactic dynamics, since the terrestrial impact cratering is usually interpreted as a result of the environmental variation during solar orbiting in the Galactic plane. The aim of this paper is to search for a long-term periodicity with a novel method since no attempt has been made so far in searching a long-term periodicity in this research field in spite of its great importance. We apply the cepstrum analysis method to the terrestrial impact cratering record for the first time. As a result of the analysis we have found noticeable peaks in the Fourier power spectrum appear ing at periods of Myrs and Myrs, which seem in a simple resonance with the revolution period of the Sun around the Galactic center. Finally we briefly discuss its implications and suggest theoretical study be pursued to explain such a long-term periodicity.

A real-time kepstrum approach to speech enhancement and noise cancellation

This paper introduces the kepstrum method of identification of acoustic transfer functions and its real-time application in techniques such as speech enhancement and noise cancellation. By using kepstrum (known elsewhere as complex cepstrum) analysis, we will show that the kepstrum method is reliable and applicable to real-time processing by providing an efficient kepstrum processing technique with a fast processing time using fast Fourier-transforms (FFTs). Furthermore, it will be shown that its application to speech enhancement methods gives several favourable effects such as: (1) application flexibility by enabling invertibility, which gives a stable minimum phase transfer function on application of the inverse of a room impulse response, (2) processing robustness by using it as building blocks and (3) computational simplicity by highly reduced weights in an adaptive algorithm when it is used with a cascaded adaptive filter. Accordingly, we will show that the kepstrum approach contributes to speech enhancement and noise cancellation with an improved performance in signal-to-noise ratio (SNR) by using only two microphones with a small physical dimension in size. Experiments on a 20 cm broadside two-microphone acoustic beamformer configuration are implemented in real-time in an indoor office.