Time Synchronous Averaging Research Articles

Wind turbine condition monitoring

Wind turbine condition monitoring

A New Hybrid "Park's Vector - Time Synchronous Averaging" Approach to the Induction Motor-fault Monitoring and Diagnosis

Induction motors are critical components in industrial processes since their failure usually lead to an unexpected interruption at the industrial plant. The studies of induction motor behavior during abnormal conditions and the possibility to diagnose different types of faults have been a challenging topic for many electrical machine researchers. In this regard, an efficient and new method to detect the induction motor-fault may be the application of the Time Synchronous Averaging (TSA) to the stator current Park’s Vector. The aim of this paper is to present a methodology by which defects in a three-phase wound rotor induction motor can be diagnosed. By exploiting the cyclostationarity characteristics of electrical signals, the TSA method is applied to the stator current Park’s Vector, allowing the monitoring of the induction motor operation. Simulation and experimental results are presented in order to show the effectiveness of the proposed method. The obtained results are largely satisfactory, indicating a promising industrial application of the hybrid Park’s Vector-TSA approach.

Spur gear tooth root crack detection using time synchronous averaging under fluctuating speed

Filtering techniques are used to improve the signal to noise ratio (SNR) for better feature extraction. The time synchronous averaging (TSA) is one of such method that is based on averaging periodic sections. However, it fails to give significant results for an asynchronous or fluctuating speed condition. Moreover, most of the real life applications of gear are in asynchronous conditions. The aim of this paper is to develop a methodology which is robust for fault detection of gears under fluctuating load and speed conditions. A multiple-pulse individually rescaled-time synchronous averaging (MIR-TSA) technique in conjunction with conventional time synchronous averaging has been proposed. A 2-D finite element methodology based on principal or linear elastic fracture mechanics is adopted for predicting the crack propagation path at the root of gear tooth. The crack has been introduced using wire electrode discharge machining (WEDM). The vibration signals were recorded using drivetrain dynamic simulator (DDS) setup for various combination of load and crack length both for constant as well as fluctuating speed. Various time domain features such as root mean square, crest factor and kurtosis have been calculated using classical TSA and proposed MIR-TSA. A comparison of different extracted features between the proposed method and classic TSA has also been outlined. It has been observed that the proposed method enhances the fault detection under fluctuating speed conditions.

Feature Extraction of Faulty Rolling Element Bearing Based on Time Synchronous Average and Cepstrum Edit

The cepstrum edit scheme for the vibration feature extraction of the faulty rolling element bearing (REB) is studied in this paper. By combined the time synchronous average (TSA) and the real cepstrum to localize and edit the cepstral lines of the original vibration, the unwanted discrete frequency components can be removed. Then, a corresponding inverse procedure is designed, in which the edited cepstrum and the original phase spectrum are employed to reconstruct the edited vibration for the REB feature extraction. Simulation verified the scheme positively.

Signal Processing Techniques to Improve an Acoustic Emissions Sensor


 
 
 Acoustic Emissions (AE) are stress waves produced by the sudden internal stress redistribution of material caused by changes in the internal structure of the material. Possible causes of these changes are crack initiation and growth, crack opening/closure, or pitting in monolithic materials (gear/ bearing material). Where as vibration can measure the effect of damage, AE is a direct measure of damage. Unfortunately, AE methodologies suffer from the need of high sample rates (4 to 10 Msps) and relatively immature algorithms for condition indictors (CI). This paper hypothesizes that the AE signature is the result of some forcing function (e.g. periodic motion in the case of rotating machinery). By using analog signal processing to demodulating the AE signature, one can reconstruct the information carried (e.g. modulation) by the AE signature and provide improved diagnostics. As most on-line condition monitoring systems are embedded system, analog signal processing techniques where used which reduce the effective sample rate needed to operate on the AE signal to those similarly found in traditional vibration processing systems. Further, by implementing another signal processing technique, time synchronous averaging, the AE signal is further enhanced. This allowed, for the first time, an AE signal to be used to identify a specific component within gearbox. This processing is tested on a split torque gearbox and results are presented.
 
 

Processing for Improved Spectral Analysis


 
 
 The Fast Fourier Transform (FFT) is the workhorse of condition monitoring analysis. The FFTs’ assumption of stationarity is often violated in rotating machinery. Even in a six second acquisition on a wind turbine, the shaft speed can change by 5%. For Shaft and Gear analysis, this is mitigated through the use of the time synchronous average. For general spectrum analysis, or bearing envelope analysis, there is no such mitigation: one hopes that the effect of variation in shaft speed is small. Presented is a time synchronous resampling algorithm which corrects for variation in shaft speed, preserving the assumption of stationarity. This allows for improved spectral analysis, such as used in bearing fault detection. This is demonstrated on a real world-bearing fault.
 
 

Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes

This paper proposes a model-based technique for detecting wear in a multistage planetary gearbox used by lifting cranes. The proposed method establishes a vibration signal model which deals with cyclostationary and autoregressive models. First-order cyclostationarity is addressed by the analysis of the time synchronous average (TSA) of the angular resampled vibration signal. Then an autoregressive model (AR) is applied to the TSA part in order to extract a residual signal containing pertinent fault signatures. The paper also explores a number of methods commonly used in vibration monitoring of planetary gearboxes, in order to make comparisons. In the experimental part of this study, these techniques are applied to accelerated lifetime test bench data for the lifting winch. After processing raw signals recorded with an accelerometer mounted on the outside of the gearbox, a number of condition indicators (CIs) are derived from the TSA signal, the residual autoregressive signal and other signals derived using standard signal processing methods. The goal is to check the evolution of the CIs during the accelerated lifetime test (ALT). Clarity and fluctuation level of the historical trends are finally considered as a criteria for comparing between the extracted CIs.This study reveals the most relevant features to be used for damage detection and condition monitoring of the gear system. It is also shown that the proposed procedure using a combination of cyclostationarity and autoregressive modeling enhance the ability to detect and diagnose mechanical wear in multi-staged planetary gears.

Acoustic measurements for the combustion diagnosis of diesel engines fuelled with biodiesels

In this paper, an experimental investigation was carried out on the combustion process of a compression ignition (CI) engine running with biodiesel blends under steady state operating conditions. The effects of biodiesel on the combustion process and engine dynamics were analysed for non-intrusive combustion diagnosis based on a four-cylinder, four-stroke, direct injection and turbocharged diesel engine. The signals of vibration, acoustic and in-cylinder pressure were measured simultaneously to find their inter-connection for diagnostic feature extraction. It was found that the sound energy level increases with the increase of engine load and speed, and the sound characteristics are closely correlated with the variation of in-cylinder pressure and combustion process. The continuous wavelet transform (CWT) was employed to analyse the non-stationary nature of engine noise in a higher frequency range. Before the wavelet analysis, time synchronous average (TSA) was used to enhance the signal-to-noise ratio (SNR) of the acoustic signal by suppressing the components which are asynchronous. Based on the root mean square (RMS) values of CWT coefficients, the effects of biodiesel fractions and operating conditions (speed and load) on combustion process and engine dynamics were investigated. The result leads to the potential of airborne acoustic measurements and analysis for engine condition monitoring and fuel quality evaluation.

Gear Fault Diagnosis Based on Time Synchronous Average and Envelope Analysis

This paper proposed a new method of gear fault diagnosis in gearbox. Mainly it stresses on the combination of time synchronous average (TSA) and envelope analysis, using TSA technique to eliminate the random noises in the gearbox. Then, the clear rotation frequency, gear mesh frequency and their harmonics can be obtained through the envelope spectrum analysis. Simulation study indicates that this method can effectively detect the gear faults with a high accuracy.

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.

Two Stage Helical Gearbox Fault Detection and Diagnosis based on Continuous Wavelet Transformation of Time Synchronous Averaged Vibration Signals

Vibration signals from a gearbox are usually very noisy which makes it difficult to find reliable symptoms of a fault in a multistage gearbox. This paper explores the use of time synchronous average (TSA) to suppress the noise and Continue Wavelet Transformation (CWT) to enhance the non-stationary nature of fault signal for more accurate fault diagnosis. The results obtained in diagnosis an incipient gear breakage show that fault diagnosis results can be improved by using an appropriate wavelet. Moreover, a new scheme based on the level of wavelet coefficient amplitudes of baseline data alone, without faulty data samples, is suggested to select an optimal wavelet.

Optimal Bayesian estimation and control scheme for gear shaft fault detection

Fault detection and diagnosis of gear transmission systems have attracted a lot of attention in recent years, but there are very few papers dealing with the early detection of shaft cracks. In this paper, a new methodology for predicting failures of a gear shaft system is presented. The time synchronous averaging (TSA) method is applied to the gear shaft vibration data, and the wavelet transform technique is then used to obtain quantitative indicators of gear shaft deterioration. System deterioration is modeled as a hidden, 3-state continuous-time homogeneous Markov process. States 0 and 1, which are not observable, represent healthy and unhealthy system conditions, respectively. Only the failure state 2 is assumed to be observable. The computed quantities, which are stochastically related to the system state, are chosen as the observation process in the hidden Markov modeling framework. The objective is to develop a method for optimally predicting impending system failures, which maximizes the long-run expected average system availability per unit time. Model parameters are estimated using the EM algorithm and an optimal Bayesian fault prediction scheme is proposed. The entire procedure is illustrated using real gear shaft vibration data.

Conditioning of a statistical indicator for the detection of an asynchronous machine rotor faults

In this paper, the cyclostationary characteristics of electrical signals will be exploited in order to detect the rotor faults of an asynchronous machine. These defects are the most complex in terms of detection since they interact with the 50 Hz carrier with a weak band occupied in frequency. The testing ground used includes an industrial three-phase wound rotor asynchronous motor of 400 V, 6.2 A, 50 Hz, 3 kW, 1385 rpm characteristics. The rotor fault has been carried out by adding an extra 40 mΩ resistance on one of the rotor phases (i.e. 10% of the rotor resistance value per phase, R r = 0.4Ω). From the stator voltage and current acquisition, and by application of the Time Synchronous Averaging (TSA) method to the stator current, the electrical signal will be conditioned in order to obtain a sensitive indicator allowing to easily distinguish the healthy cases from defective ones.

Gearbox Vibration Source Separation by Integration of Time Synchronous Averaged Signals

This paper describes a simple approach for integrating all the time synchronous average (TSA) signals from multiple shafts of a gearbox to generate a composite time synchronous average which can be subtracted from the original signal to generate a second-order cyclostationary residual. This approach is compared with other techniques including an all-shaft TSA over the least common multiple of shaft rotation periods, high-pass filtering, and self-adaptive noise cancellation (SANC). The results demonstrate that the proposed approach produces an integrated TSA signal that includes only the shaft components, gear mesh components and the sidebands associated with all the shafts, while the residual contains the random vibration components and noise. The results produced by three alternative techniques do not separate the components as well or have a lower signal-to-noise ratio.

Virtual prototype and experimental research on gear multi-fault diagnosis using wavelet-autoregressive model and principal component analysis method

Gear systems are an essential element widely used in a variety of industrial applications. Since approximately 80% of the breakdowns in transmission machinery are caused by gear failure, the efficiency of early fault detection and accurate fault diagnosis are therefore critical to normal machinery operations. Reviewed literature indicates that only limited research has considered the gear multi-fault diagnosis, especially for single, coupled distributed and localized faults. Through virtual prototype simulation analysis and experimental study, a novel method for gear multi-fault diagnosis has been presented in this paper. This new method was developed based on the integration of Wavelet transform (WT) technique, Autoregressive (AR) model and Principal Component Analysis (PCA) for fault detection. The WT method was used in the study as the de-noising technique for processing raw vibration signals. Compared with the noise removing method based on the time synchronous average (TSA), the WT technique can be performed directly on the raw vibration signals without the need to calculate any ensemble average of the tested gear vibration signals. More importantly, the WT can deal with coupled faults of a gear pair in one operation while the TSA must be carried out several times for multiple fault detection. The analysis results of the virtual prototype simulation prove that the proposed method is a more time efficient and effective way to detect coupled fault than TSA, and the fault classification rate is superior to the TSA based approaches. In the experimental tests, the proposed method was compared with the Mahalanobis distance approach. However, the latter turns out to be inefficient for the gear multi-fault diagnosis. Its defect detection rate is below 60%, which is much less than that of the proposed method. Furthermore, the ability of the AR model to cope with localized as well as distributed gear faults is verified by both the virtual prototype simulation and experimental studies.

Gearbox Damage Diagnosis usingWavelet Transform Technique

Vibration-based schemes are founded on the assumption that vibration signals from gearboxes measured using accelerometers reflect their condition accurately. A large number of vibration based techniques are used to make this reflection. They include various spectral analyses such as traditional Fourier transform, short-time Fourier transform, amplitude phase modulation and time synchronous averaging and non-parametric special estimation. Recently, Wavelet Transform (WT) has been proven to be more suitable for analysis of vibration signals, since most of the time-vibration signals have instantaneous impulse trains and exhibit a transient (non-stationary) nature. This paper uses an adaptive wavelet filter, based on the Morlet wavelet, applied on the torsional vibration data measured from a single-stage gearbox with artificially induced cracks in the gear. This is done to extract some parameters and check their diagnostic behavior in an effort to search for those with the most potential and appropriateness for future health monitoring schemes. The results demonstrate that the adaptive wavelet filter is found to be very effective in detection of symptoms from vibration signals of a gearbox with early tooth cracks. Moreover the influence of crack depth, speed, and load on the wavelet entropy are interduced. Multi-hour tests were conducted and recordings were acquired using torsional vibration monitoring. The transitions in the wavelet entropy values with the recording time were highlighted suggesting critical changes in the operation of the gearbox.

TIME-FREQUENCY ANALYSIS BY EVOLUTIONARY PERIODOGRAM WITH APPLICATION IN GEAR FAULT DIAGNOSIS

The evolutionary periodogram has been introduced to mechanical fault diagnosis and relationship between the evolutionary periodogram and time-frequency spectrogram has been investigated. The evolutionary periodogram is unveiled as an especially windowed spectrogram, and is applied to gearbox fault diagnosis. It has been shown that the window used in the evolutionary periodogram is not a single function but a combination of a set of functions. Two cases of gearbox diagnosis are presented as examples of application. Vibration signals and a synchronous signal are collected for the analysis. The time synchronous averaging is used to reduce background noise or random transients to enhance the periodicity of a specific gear rotation. The performance of the evolutionary periodogram has been compared with the spectrogram for gear diagnosis, showing that the evolutionary periodogram is an alternative technique in time-frequency analysis for fault detection and better resolution can be obtained as more choices are offered by the way of constructing the window.

Novel adaptation of the demodulation technology for gear damage detection to variable amplitudes of mesh harmonics

In this paper, a novel adaptive demodulation technique including a new diagnostic feature is proposed for gear diagnosis in conditions of variable amplitudes of the mesh harmonics. This vibration technique employs the time synchronous average (TSA) of vibration signals. The new adaptive diagnostic feature is defined as the ratio of the sum of the sideband components of the envelope spectrum of a mesh harmonic to the measured power of the mesh harmonic. The proposed adaptation of the technique is justified theoretically and experimentally by the high level of the positive covariance between amplitudes of the mesh harmonics and the sidebands in conditions of variable amplitudes of the mesh harmonics. It is shown that the adaptive demodulation technique preserves effectiveness of local fault detection of gears operating in conditions of variable mesh amplitudes.

Advanced Signal Processing Tools for the Vibratory Surveillance of Assembly Faults in Diesel Engine Cold Tests

This paper addresses the use of several signal processing tools for monitoring and diagnosis of assembly faults in diesel engines through the cold test technology. One specific fault is considered here as an example: connecting rod with incorrectly tightened screws. First, the experimental apparatus concerning the vibration tests is introduced. Subsequently, the dynamic analysis of the engine has been carried out in order to calculate the connecting rod forces against the crankpin for predicting the position where mechanical impacts are expected. Then, a vibration signal model for this type of faults is introduced. It deals with the cyclostationary model in which the signal is subdivided into two main parts: deterministic and nondeterministic. Finally, the acceleration signals acquired from the engine block during a cold test cycle at the end of the assembly line are analyzed. For quality control purposes in order to obtain reliable thresholds for the pass/fail decision, a method based on the image correlation of symmetrized dot patterns is proposed. This method visualizes vibration signals in a diagrammatic representation in order to quickly detect the faulty engines in cold tests. Moreover, the fault identification is discussed on the basis of the cyclostationary model of the signals. The first-order cyclostationarity is exploited by the analysis of the time synchronous average (TSA). In addition, the residual signal is evaluated by subtracting the TSA from the raw synchronized signal, and thus, the second-order cyclostationarity analysis is developed by means of the Wigner–Ville distribution (WVD), Wigner–Ville spectrum (WVS), and mean instantaneous power. Moreover, continuous wavelet transform is presented and compared with the WVD and WVS.

Gearbox Fault Diagnosis Based on Vibration Signals Measured Remotely

In many cases, it is impractical to measure the vibrations directly at or close to their source. It is a common practice to measure the vibration at a location far from the source for condition monitoring purposes. The vibration measured in this way inevitably has high distortions from the vibrations due to the effect of the attenuation of signal paths and the interference from other sources. The suppression of the distortions is a key issue for the remote measurements based condition monitoring. In this paper, the influences of transducer locations are investigated on a typical gearbox transmission system for the detection of the faults induced to the gearbox. Several signal processing techniques’ analysis results show that the attenuation and interference cause high influences on the gear transmission signals. However, time synchronous average (TSA) is very effective to detect the local faults induced to the gear system.