Time Synchronous Averaging Research Articles

On the identification of the angular position of gears for the diagnostics of planetary gearboxes

Generally, in planetary gearbox diagnostics, vibration transducers are placed on the gearbox case near the ring gear. The relative angular position of the planet gears with respect to the transducer is a useful information for the evaluation of vibration signals related to planet/sun gears. This angular position is usually unknown, or it is known with a large tolerance causing serious difficulties in both gears and bearing diagnostics. In fact, noise and spurious component from healthy planets could overhang the informative content about incipient faults. The present work seeks to propose two alternative methods for the identification of the angular position of the planet gears with respect to the transducer. The first one is based on the study of how the power flows inside the Time Synchronous Average of the ring gear, whilst the second method is based on a modified statistical parameter such as the Crest Factor. The effectiveness of these methods is assessed on the basis of actual vibration signals acquired from a faulty planetary gearbox. The knowledge of the exact angular position of the planet gears allows the diagnostics of both gears and bearings, as proven by extensive experimental activities reported in the paper.

Gear crack detection using modified TSA and proposed fault indicators for fluctuating speed conditions

Fault diagnosis of gearbox at an early stage is very important to avoid catastrophic failures. Condition indicators (CI) are used to quantify the level of vibration generated by the defected gears. A comprehensive comparison of various CIs, i.e., RMS, kurtosis, crest factor, FM0, FM4, M6A, NB4, energy ratio, NA4, energy operator and two new proposed condition indicators (PS-I and PS-II), have been performed for no crack, initial crack and advanced crack for different fluctuating profiles of input speed. This comparative analysis shows the responsiveness of indicators towards crack detection. Limited speed fluctuations and instantaneous high speed fluctuations are considered. Results suggest that newly proposed condition indicators are more robust, stable and effective towards crack detection under fluctuating profiles of speed. A modified time synchronous averaging (MTSA) is also proposed to increase signal-to-noise ratio (SNR).

Repressing the effects of variable speed harmonic orders in operational modal analysis

Discrete frequency components such as machine shaft orders can disrupt the operation of normal Operational Modal Analysis (OMA) algorithms. With constant speed machines, they have been removed using time synchronous averaging (TSA). This paper compares two approaches for varying speed machines. In one method, signals are transformed into the order domain, and after the removal of shaft speed related components by a cepstral notching method, are transformed back to the time domain to allow normal OMA. In the other simpler approach an exponential shortpass lifter is applied directly in the time domain cepstrum to enhance the modal information at the expense of other disturbances. For simulated gear signals with speed variations of both ±5% and ±15%, the simpler approach was found to give better results The TSA method is shown not to work in either case. The paper compares the results with those obtained using a stationary random excitation.

Vibration-based Fault Diagnostic of a Spur Gearbox

This paper presents comparative studies of Fast Fourier Transform (FFT), Short Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) as several advanced time-frequency analysis methods for diagnosing an early stage of spur gear tooth failure. An incipient fault of a chipped tooth was investigated in this work using vibration measurements from a spur gearbox test rig. Time Synchronous Averaging was implemented for the analysis to enhance the clarity of fault feature from the gear of interest. Based on the experimental results and analysis, it was shown that FFT method could identify the location of the faulty gear with sufficient accuracy. On the other hand, Short Time Fourier Transform method could not provide the angular location information of the faulty gear. It was found that the Continuous Wavelet Transform method offered the best representation of angle-frequency representation. It was not only able to distinguish the difference between the normal and faulty gearboxes from the joint angle-frequency results but could also provide an accurate angular location of the faulty gear tooth in the gearbox.

Time-Frequency Enhancement Technique for Bevel Gear Fault Diagnosis

Gear failure is one of the most common causes of breakdown in rotating machineries. It is well known that vibration signals from machineries can be effectively used to detect certain gear faults. Yet it is still not an easy task to find a symptom that reflects a particular fault from vibration signals. This paper presents an advanced time-frequency signal processing technique for extracting important gear fault information from the vibration signal that is heavily corrupted by measurement noise. Experiments were performed on a bevel gearbox test rig using vibration measurements. The Time Synchronous Average (TSA) was initially utilized to eliminate all asynchronous component of vibration signal obtained from the gear. The Continuous Wavelet Transform (CWT) method was then used to capture the non-stationary behaviour of the impulse signal generated from the broken bevel gear tooth. It was shown that the diagnosis method using the Continuous Wavelet Transform combined with Time Synchronous Averaging outperformed the conventional spectral analysis, capable of identifying the angular location of broken teeth in the gear.

Feature Extraction Using Discrete Wavelet Transform for Gear Fault Diagnosis of Wind Turbine Gearbox

Vibration diagnosis is one of the most common techniques in condition evaluation of wind turbine equipped with gearbox. On the other side, gearbox is one of the key components of wind turbine drivetrain. Due to the stochastic operation of wind turbines, the gearbox shaft rotating speed changes with high percentage, which limits the application of traditional vibration signal processing techniques, such as fast Fourier transform. This paper investigates a new approach for wind turbine high speed shaft gear fault diagnosis using discrete wavelet transform and time synchronous averaging. First, the vibration signals are decomposed into a series of subbands signals with the use of a multiresolution analytical property of the discrete wavelet transform. Then, 22 condition indicators are extracted from the TSA signal, residual signal, and difference signal. Through the case study analysis, a new approach reveals the most relevant condition indicators based on vibrations that can be used for high speed shaft gear spalling fault diagnosis and their tracking abilities for fault degradation progression. It is also shown that the proposed approach enhances the gearbox fault diagnosis ability in wind turbines. The approach presented in this paper was programmed in Matlab environment using data acquired on a 2 MW wind turbine.

Bearing diagnosis using proximity probe and accelerometer

Employing multiple sensors that generate different physical parameters from the measured system to monitor its health increases the diagnosis reliability. In the present work, bearing diagnosis capabilities of proximity probes are explored by exploiting its advantages and alleviating its shortcomings using appropriate signal processing of the raw time domain data. A Time Synchronous Averaging based method is proposed for processing of the data acquired by proximity probes and its benefit is illustrated on test bearings. Simultaneous synchronous data is acquired with the help of proximity probes and accelerometer during a life test as the defect is naturally induced and progressed with time. The proximity probe is shown to perform better diagnosis for inner race defect compared to accelerometer due to a direct transmission path for this defect. The use of proximity probe can effectively supplement the information from accelerometer and improve the accuracy of bearing diagnosis.

On the Detection of Induction-Motor Rotor Fault by the Combined “Time Synchronous Averaging-Discrete Wavelet Transform” Approach

Induction motors are widely used in industrial processes since they offer a very high degree of reliability. But like any other machine, they are vulnerable to faults, which if left unmonitored, might lead to an unexpected interruption at the industrial plant. Therefore, the condition monitoring of the induction motors have been a challenging topic for many electrical machine researchers. Indeed, the effectiveness of the fault diagnosis and prognosis techniques depends very much on the quality of the fault features selection. However, in induction-motor drives, rotor defects are the most complex in terms of detection since they interact with the supply frequency within a restricted band around this frequency, especially in the no-loaded case. To overcome this drawback, this paper deals with an efficient and new method to diagnose the induction-motor rotor fault based on the digital implementation of the monitoring algorithm based on the association of the Time Synchronous Averaging technique and Discrete Wavelet Transform. 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 combined “Time Synchronous Averaging – Discrete Wavelet Transform” approach.

Rolling element bearing defect diagnosis under variable speed operation through angle synchronous averaging of wavelet de-noised estimate

Rolling element bearings are widely used in rotating machines and their faults can lead to excessive vibration levels and/or complete seizure of the machine. Under special operating conditions such as non-uniform or low speed shaft rotation, the available fault diagnosis methods cannot be applied for bearing fault diagnosis with full confidence. Fault symptoms in such operating conditions cannot be easily extracted through usual measurement and signal processing techniques. A typical example is a bearing in heavy rolling mill with variable load and disturbance from other sources.In extremely slow speed operation, variation in speed due to speed controller transients or external disturbances (e.g., varying load) can be relatively high. To account for speed variation, instantaneous angular position instead of time is used as the base variable of signals for signal processing purposes. Even with time synchronous averaging (TSA) and well-established methods like envelope order analysis, rolling element faults in rolling element bearings cannot be easily identified during such operating conditions. In this article we propose to use order tracking on the envelope of the wavelet de-noised estimate of the short-duration angle synchronous averaged signal to diagnose faults in rolling element bearing operating under the stated special conditions. The proposed four-stage sequential signal processing method eliminates uncorrelated content, avoids signal smearing and exposes only the fault frequencies and its harmonics in the spectrum. We use experimental data11This article includes complete dataset as supplementary material (accessible from online version). from a laboratory setup to validate the diagnosis tool for bearing raceway and rolling element faults.

Autocorrelation-based time synchronous averaging for condition monitoring of planetary gearboxes in wind turbines

We propose autocorrelation-based time synchronous averaging (ATSA) to cope with the challenges associated with the current practice of time synchronous averaging (TSA) for planet gears in planetary gearboxes of wind turbine (WT). An autocorrelation function that represents physical interactions between the ring, sun, and planet gears in the gearbox is utilized to define the optimal shape and range of the window function for TSA using actual kinetic responses. The proposed ATSA offers two distinctive features: (1) data-efficient TSA processing and (2) prevention of signal distortion during the TSA process. It is thus expected that an order analysis with the ATSA signals significantly improves the efficiency and accuracy in fault diagnostics of planet gears in planetary gearboxes. Two case studies are presented to demonstrate the effectiveness of the proposed method: an analytical signal from a simulation and a signal measured from a 2kW WT testbed. It can be concluded from the results that the proposed method outperforms conventional TSA methods in condition monitoring of the planetary gearbox when the amount of available stationary data is limited.

Improved Fault Detection by Appropriate Control of Signal Bandwidth of the TSA


 
 
 Vibration analysis is perhaps the longest serving technology used in condition monitoring. It is usually assumed that higher sampling rates improve fault detection due to the increased bandwidth of the acquisition. That said, increased bandwidth may decrease the signal to noise, impairing fault detection. Alternatively, if the fault features’ bandwidth is greater than the system bandwidth, the fault cannot be observed. One tool of vibration analysis is the Time Synchronous Average (TSA) analysis. Statistics of the TSA itself can be used as a fault feature, or statistics based on analysis performed on the TSA (energy operator, residual analysis, amplitude/frequency modulation analysis) are used as fault features. Additionally, the computation of the TSA requires a tachometer signal for zero crossing, which has its own bandwidth effect on the TSA analysis. This paper discusses bandwidth control techniques to improve fault detection using the TSA. The techniques are validated using real world pinion data. These techniques have other advantages for embedded condition monitoring systems.
 
 

Vibration-based Fault Diagnostics of Planetary Gearbox Using Time Synchronous Averaging with Multiple Window Functions


 
 
 Recently, numerous number of studies have been made to advance TSA for vibration-based diagnostics of planet gears of a planetary gearbox. To increase signal-to-noise ratio of the vibration signals, various narrow-range window functions centered on sensor’s position have been developed to capture the instances when the planet gears are adjacent to the sensor. However, TSA with such narrow-range window functions is unable to detect the abnormal signal if it is amplified at outside of the sensor’s position due to an unexpected vibration modulation characteristics of the gearbox. This paper proposes a TSA which is robust toward the unexpected vibration modulation characteristics of the gearbox. Multiple narrow-range window functions were employed to perform multiple TSAs rather than employing the sole window function centered on the sensor’s position. Condition indicators with regard to every ring gear’s teeth were derived, and accumulated for the purpose of condition monitoring. Then, optimal position of the window function was determined to maximize capability to detect signals from the faulty gear. For demonstration of the proposed TSA, test was performed with a 2kW testbed having one-stage planetary gearbox within which a planet gear with artificial fault was assembled.
 
 

Application of an improved dynamic time synchronous averaging method for fault diagnosis in conditions of speed fluctuation and no tachometer

Time synchronous averaging has been widely used for machinery fault diagnosis. However, it cannot reveal signal characteristics accurately in conditions of speed fluctuation and no tachometer due to the phase accumulation error. In this paper, an improved dynamic-time synchronous averaging method is proposed to extract the periodic feature signal from the fluctuated vibration signal for fault detection when no tachometer signal is available. In this method, empirical mode decomposition, dynamic time warping, and time synchronous averaging are performed on gear vibration signals to detect fault characteristic information. First, empirical mode decomposition is performed on the vibration signal and a series of intrinsic mode functions are produced. The sensitive intrinsic mode functions providing fault-related information are selected and reconstructed and the corresponding envelop signals are equal-space intercepted. Then, the phase accumulation error among the envelop signal segments is estimated by the dynamic time warping, which is further used to compensate the phase accumulation error between the intrinsic mode function segments of the reconstructed signal. Finally, the compensated intrinsic mode function segments are averaged to obtain the feature signal. Simulation analysis shows the advantages of the proposed method in extracting faulty feature signal from speed fluctuation signal without tachometer and identifying gear fault. Experiments with both normal and faulty gear were conducted and the vibration signals were captured. The proposed method is applied to identify the gear damage and the diagnosis results demonstrate its superiority than other methods.

Hybridization of time synchronous averaging, singular value decomposition, and adaptive neuro fuzzy inference system for multi-fault bearing diagnosis

Bearing diagnosis has attracted considerable research interest; thus, researchers have developed several signal processing techniques using vibration analysis to monitor the rotating machinery’s conditions. In practical engineering, features extraction with most relevant information from experimental vibration signals under variable operation conditions is still regarded as the most critical concern. Therefore, actual works focus on combining Time Domain Features (TDFs) with decomposition techniques to obtain accurate results for defect detection, identification, and classification. In this paper, a new hybrid method is proposed, which is based on Time Synchronous Averaging (TSA), TDFs, and Singular Value Decomposition (SVD) for the feature extraction, then the Adaptive Neuro-Fuzzy Inference System (ANFIS) which gathers the advantages of both neural networks and fuzzy logic is applied for the classification process. First, TSA is used to reduce noises in the vibration signal by extracting the periodic waveforms from the disturbed data; thereafter, TDFs are applied on each synchronous signal to construct a feature matrix; afterwards, SVD is performed on the obtained matrices to remove the instability of statistical values and select the most stable vectors. Finally, ANFIS is implemented to provide a powerful automatic tool for features classification.

Survey of Condition Indicators for Condition Monitoring Systems


 
 
 Currently, the wind energy industry is swiftly changing its maintenance strategy from schedule based maintenance to predictive based maintenance. Condition monitoring systems (CMS) play an important role in the predictive maintenance cycle. As condition monitoring systems are being adopted by more and more OEM and O&M service providers from the wind energy industry, it is crucial to effectively interpret the data generated by the CMS and initiate proactive processes to efficiently reduce the risk of potential component or system failure which often leads to down tower repair or gearbox replacement. The majority of CMS are designed and constructed based on vibration analysis which has been refined over the years by researchers and scientists. This paper provides detailed description and mathematical interpretation of a comprehensive selection of condition indicators for gears, bearings and shafts. Since different condition indicators are sensitive to different kind of failure modes, the application for each condition indicators were also discussed. The Time Synchronous Averaging (TSA) algorithm was applied as the signal processing method before the extraction of condition indicators for gears and shafts. Time Synchronous Resampling algorithm was applied to stabilize the shaft speed before the extraction of bearing condition indicators. Several case studies of real world wind turbine component failure detection using condition indicators were presented to demonstrate the effectiveness of certain condition indicators.
 
 

A Vibration-Based Approach for Stator Winding Fault Diagnosis of Induction Motors: Application of Envelope Analysis


 
 
 Induction motors are usually considered as one of the key components in various applications. To maintain the availability of induction motors, it calls for a reliable condition monitoring and prognostics strategy. Among the common induction motor faults, stator winding faults are usually diagnosed with current and voltage signals. However, if the same performance can be achieved, the use of vibration signal is favorable because the winding fault diagnostic method can be integrated with bearing fault diagnostic method which has been successfully proven with vibration signal. Existing work concerning vibration for winding faults often takes it either as auxiliary to magnetic flux, or is not able to detect the winding faults unless severity is already quite significant. This paper proposes a winding fault diagnostic method based on vibration signals measured on the mechanical structure of an induction motor. In order to identify the signature of faults, time synchronous averaging was firstly applied on the raw vibration signals to remove discrete frequency components originating from the dynamics of the shaft and/or gears, and the spectral kurtosis filtering was subsequently applied on the residual signal to emphasize the impulsiveness. For the purpose of enhancing the residual signal in practice, a demodulation technique was implemented with the help of kurtogram. A series of experiments have been conducted on a three-phase induction motor test bed, where stator inter-turn faults can be easily simulated at different loads, speeds and severity levels. The experimental results show that the proposed method was able to detect inter-turn faults in the induction motor, even when the fault is incipient.
 
 

Effect of parameters setting on performance of discrete component removal (DCR) methods for bearing faults detection

Separation between non-deterministic and deterministic components of gearbox vibration signals has been considered as important signal processing step for rolling-element bearing fault diagnostics. In this paper, the performance of bearing fault detection after applying various discrete components removal (DCR) methods is quantitatively compared. Three methods that have become widely used, namely (i) time synchronous average, (ii) self adaptive noise cancellation (SANC) and (iii) cepstrum editing, were considered. The three DCR methods with different parameter settings have been applied to vibration signals measured on two different gearboxes. In general, the experimental results show that cepstrum editing method outperforms the other two methods.

Fault Diagnostics of Planet Gears in Wind Turbine Using Autocorrelation- based Time Synchronous Averaging (ATSA)

A planetary gearbox is widely used in various rotating systems because it can be used as a speed reducer or increaser without change in direction of shaft while transferring great driving power. Despite many attempts it is still challenging to diagnose potential faults of the planetary gearbox because of multiple contacts and axis rotation of planet gears resulting in complex vibration characteristics. This paper thus presents an original method to isolate vibration signals induced by the planet gears from the complex vibration signals for fault diagnostics of the planetary gearbox. First, an in-depth study on the vibration characteristics of planet gears is presented using the autocorrelation function of the vibration signal. The autocorrelation-based time synchronous averaging (ATSA) method is then developed for the isolation of the vibration signals produced by the planet gears. The vibration signals were utilized for extracting health related data which facilitate the efficient fault diagnostics of the planet gears. Case study with a wind turbine testbed showed that the proposed method can diagnose the root crack of the planet gears.

Fault detection in a multistage gearbox by time synchronous averaging of the instantaneous angular speed

The paper deals with an instantaneous angular speed (IAS) based algorithm for fault detection in a multistage gearbox. Fast Fourier transform (FFT) is a well established technique for analysis of a stationary signal. However, the IAS signal from the multistage gearbox is a combination of periodic signal and structure borne noise. This structure borne noise makes the IAS signal non stationary. Hence, FFT is no longer suitable for this non stationary signal analysis. Therefore in this paper, the IAS signal is time synchronously averaged with respect to lay shaft speed and then followed by FFT reveals the gear mesh frequency and rotational frequencies which are synchronous with the gearbox’s rotating speed. Thus, the algorithm can accurately and reliably detect the fault in multistage gearbox using the IAS. The paper also investigated the behavior of IAS signal for three different gearboxes (healthy, one tooth and two teeth broken) under two different speeds and three different loading conditions.

Wind turbine condition monitoring

Wind turbine condition monitoring