Time Synchronous Averaging Research Articles

Use of Tach from Vibration to Estimate Bearing Spall Length

Vibration analysis is often used for bearing fault diagnostics. Envelope analysis or other cyclo-stationary processes can capture a fault feature or condition indicator that is correlated to the spall length. However, no study has defined a process for estimating spall length on real-world data. The problem is that the spall length is a time-domain property of the signal. This paper generates a synthetic tachometer signal from the fault itself. It is synchronous to the rolling element exit from the spall, allowing for a time-domain representation of waveform using the time synchronous average. From this, an estimate of the length of the bearing fault can be determined.

A novel model-based Cauchy-Schwarz divergence condition indicator for gears monitoring during fluctuating speed conditions

Gear monitoring and fault diagnosis are vital for preventing accidents and minimizing economic losses in transportation and industrial systems. Traditional methods use vibration sensors and a two-stage analysis approach: preprocessing data to remove noise and extract relevant components, and generating a condition indicator to detect behavioral anomalies in the gears over time.Time synchronous averaging is a notable tool for monitoring gears at constant speeds. Such a tool filters sensor signals and extracts rotation-related components by using statistical measurements as condition indicators. However, it has limitations in scenarios with time-varying sampling rates and fluctuating speeds, where statistical measures may not fully capture changes in system parameters.This article proposes a novel methodology for monitoring gears in multivariate rotordynamical systems under fluctuating speed conditions. The method integrates time synchronous averaging, system identification algorithms, and statistical tools. It generates a time-synchronous average signal considering speed fluctuations, computes a state–space model of gear behavior in healthy states, extracts residual data from a data-driven model, and generates a condition indicator based on the Cauchy–Schwarz divergence.The proposed methodology was evaluated using experimental data from three rotor dynamical setups under different operational conditions. Validation showed its effectiveness, especially under high-load conditions with significant speed fluctuations.

Sliding time synchronous averaging based on independent extended autocorrelation function for feature extraction of bearing fault

Extracting fault feature is a key challenge during the early failure stage of bearings due to weak fault components and strong background noises. To solve this problem, an extension operation is introduced, and a novel algorithm called independent extended autocorrelation function is proposed. Subsequently, based on time synchronous averaging, the sliding time synchronous averaging method is developed to further enhance periodic components. The time dimension is added to generate a series of periodic impulses, which is advantageous to directly extract the fault characteristic. Therefore, the presented algorithm is called the sliding time synchronous averaging based on independent autocorrelation function (STSA-IEACF). Numerically simulated signals and two experimental datasets are employed to compare the performance of the STSA-IEACF with the mainstream blind deconvolution methods in fault feature extraction of bearing. The results show that the proposed algorithm performs better than the others in this terms of extracting fault characteristics.

Theory, validation, and improvement of four enhancement algorithms for repetitive impulses

Analyzing vibration of rotating machinery signals is a popular methodology derived on the potent tools provided by cyclostationary process theory. Among them, the autocorrelation function (ACF), the Fourier transform (FT), the short-time Fourier transform (STFT), and the time synchronous average (TSA) are widely used for enhancing and analyze random cyclostationary components, i.e., repetitive impulses. This paper aims to thoroughly investigate these techniques through a comprehensive scientific research process. Initially, theoretical analyses are conducted to discuss their noise suppression principles, on the foundation of mean and variance analysis. Results indicate that for Gaussian random noise, the variance of the noise linked to the ACF, the FT, and TSA almost decreases linearly with signal length, while the noise variance of the short-time Fourier transform does so with window length. Therefore, longer signal lengths for the ACF, the FT, and the TSA, or extended window lengths for the STFT, improve their denoising performance. Subsequently, a series of simulated signals are generated to validate these findings, complemented by public datasets for further verification. Finally, the paper discusses and concludes with enhancement approaches derived from these four methodologies.

Detect Gear Fault in Wind Turbine Gearbox based on Time Synchornous Averaging Without Phase Signal.

Wind energy is increasingly recognized as a worldwide sustainable and environmentally friendly power source. Nevertheless, an important barrier to further investment in wind energy is the large rate of failure that occurs to turbines. The gearbox, a crucial component, has a major effect on the performance of wind turbines. It consists of complex planetary and cylindrical gear systems, making it prone to failure and causing major defects in wind turbines. Therefore, there is an urgent need to minimize downtime and improve productivity in wind turbine gearbox operations. Recent decades have witnessed growing interest in fault diagnosis of gearboxes due to their widespread use and industry significance. The time synchronous averaging (TSA) method is widely used as a fundamental approach to detect faults in wind turbine gearboxes from mechanical vibration signals. Usually, applying this method requires a device to measure the vibration phase. Nevertheless, there are certain circumstances where installing a phase-measuring device might pose challenges. For instance, if the gearbox operates, it cannot be paused for installation. Additionally, if the gearboxes are enclosed, it becomes difficult to insert the device. The present paper presents an innovative technical approach to improve the time synchornous averaging method without requiring information about phase vibration. It also evaluates the effectiveness of the proposed method using feature values. An experimental test rig was set up to evaluate the algorithm's effectiveness, simulating different gear faults and load conditions.

Incorporating the Time-synchronous Averaging Method into Vibration Analysis Methodologies for the Detection and Localization of Bearing Defects

Objective: The objectives of this paper are to highlight the significance of vibration analysis, especially in predictive maintenance for rotating machinery, and to emphasize the importance of detecting bearing defects that may result in machinery failure. Methods: The proposed methodology combines the use of time-synchronous averaging (TSA) with existing vibration analysis techniques. TSA involves aligning vibration data with specific events or phases in the machinery's operation, such as shaft rotation. By synchronizing the data in this way, the methodology aims to reduce noise and enhance the signal related to bearing defects, making them more distinguishable. Additionally, the methodology incorporates well-established vibration analysis techniques. These techniques may include frequency analysis, amplitude modulation analysis, waveform analysis, and others commonly used in the field of condition monitoring and predictive maintenance. Results: The results of the analysis begin with waveform analysis, which involves examining the shape and pattern of vibration signals captured from the pinion. This analysis provides valuable insights into the dynamic behavior of the pinion gear, including any variations or abnormalities in its motion. Moreover, the use of synchronized waveforms is crucial in this analysis. By aligning the vibration data with specific events or phases in the gear mesh cycle, such as tooth engagement, the analysis can pinpoint moments when potential faults or wear in the machinery may occur. This synchronization allows for a more precise assessment of the vibration signals, enabling the detection of irregularities that may indicate underlying issues with the pinion or other components of the machinery. Conclusion: A pivotal aspect of the methodology involves envelope spectra analysis, significantly enhancing diagnostic capabilities. This analysis identifies fault patterns that might not be readily apparent in conventional vibration analysis. The incorporation of envelope spectra proves instrumental in proactive maintenance, enabling early detection of potential issues. This, in turn, contributes to the overall reliability and optimization of machinery performance.

WIRELESS LAN DESIGN AND FUTURE INTERNET ARCHITECTURE

In this research thesis, an advance work is being done on the Wireless Local Area Network (WLAN) design and future internet architecture for intrusion detection. There has been a large shift worldwide in using technology in almost every aspect of our lives. This trend has been termed in many industries as the "Internet of Things" (IoT), where the Internet and cloud- based activities are seeing more and more use for control, monitoring and data storage applications. As stated earlier, these assumptions were made because of the extreme difficulty in obtaining any related telemetry from an actual WLAN system or business-critical network. To improve the validity of the simulation, it would be beneficial to obtain even sanitized telemetry from an actual system. Then, the simulation could be refined to better reflect the dynamics of the signals in a real system. If actual WLAN system data is unobtainable, then the test by itself can be modified to reflect more processes seen in these systems. First, a small flow loop with sensing and control elements could be constructed and driven by the servers of the test bed. The primary server in the test bed that acts as the WLAN server could then be programmed to poll the sensors, issue commands and collect data. Time synchronous averaging techniques are used to resample telemetry sources that have different sampling rates. The research provides several case studies to prove the efficacy of the newly developed variable grouping technique for WLAN. Recall that variable grouping methods are needed to extract sets or subsets of variables that give the lowest model prediction errors. The research also presents AAKR and AAMSET model results for several successfully detected intrusion activities from two different data sets. The models utilized four different clusters of telemetry taken from the Linux kernel to determine which class of telemetry was more effective for intrusion detection. The knowledge-based system is certainly the most numerous and widely used type that uses signatures or features of known wireless network attacks for detection purposes. While this type is easy to implement and has a low rate of false/missed alarms, it will always miss the so-called zero-day wireless network attacks. Recall that a zero-day wireless network attack is a new, unknown intrusion. Because the knowledge-based system has never seen a zero-day intrusion event, it will classify this new behavior as normal. In contrast, the behavior-based system can detect known and zero-day wireless network attacks with an accuracy of 97.86% correctly. This type uses data-driven modeling techniques to learn normal system behavior. Once trained, any future monitored telemetry that deviates from this learned behavior will be labeled as an intrusion event.

Mixed Eccentricity Fault Detection for Induction Motors Based on Time Synchronous Averaging of Vibration Signals

This paper presents a method based on vibration signals to diagnose rotor mixed eccentricity faults in three-phase squirrel cage induction motors (SCIMs). Rotor eccentricity is a typical fault in SCIMs that significantly affects the performance of electrical machines and results in unwanted vibrations. The time synchronous averaging (TSA) signal, which consists of the harmonics components generated by the fault, is obtained via the characteristic frequency of the eccentricity fault. TSA is decomposed into two signals: TSA-regular and TSA-difference, which are respectively similar to approximate and details of wavelet transform. Based on the fault index, which is the maximum FFT value of the TSA-difference signal, the healthy and faulty motors can be distinguished. The proposed approach is evaluated on an experimental test rig equipped with a 1.5 kW SCIM supplied directly from the power grid under various load conditions. The results illustrate the effectiveness of the proposed approach in detecting the rotor mixed eccentricity faults.

Early pitting fault detection for polymer gears using kurtosis-VMD based condition indicators

The vibration signals of a polymer gear are considerably weak and susceptible to ambient noise at the early stage of the fault, which makes the fault difficult to detect. Efficient detection of an early fault in a polymer gear may improve the operation safety of the machinery system that utilizes it for power transmission. This study introduces an innovative approach for the early detection of pitting faults in polymer gears, utilizing condition indicators (CIs) derived from kurtosis-variational mode decomposition (VMD). First, the vibration signal of the polymer gear is decomposed using VMD into several components. Second, the sensitive components are selected to construct a new signal from the first two largest kurtosis values. Third, the CIs are extracted from newly constructed signals, and envelope spectrum analysis is performed. It is observed from the results that the kurtosis-VMD based CIs are effective in the early pitting fault detection of polymer gears. Finally, it is found that the proposed method performs better in all operating conditions considered in the experiment, compared with raw signal and kurtosis-empirical mode decomposition (EMD) based analysis. The proposed method’s response to noise is also explored. Furthermore, the proposed method is compared with the existing time synchronous averaging (TSA), difference, and residual methods.

An Envelope Time Synchronous Averaging for Wind Turbine Gearbox Fault Diagnosis

An Envelope Time Synchronous Averaging for Wind Turbine Gearbox Fault Diagnosis

Application of Time Synchronous Averaging in Mitigating UAV Noise and Signal Loss for Continuous Scanning Laser Doppler Vibrometry

The laser Doppler vibrometer (LDV) has been shown to be effective for a wide application of vibration assessments that are well accepted. One of the new avenues for exploring alternative measurement scenarios, mounting LDVs on unmanned aerial vehicles (UAVs) is emerging as a potential avenue for remote and harsh environment measurements. Such configurations grapple with the challenge of the LDV sensor head being sensitive to UAV vibration during flight and signal loss due to tracking error. This study investigates the effectiveness of several Time Synchronous Averaging (TSA) techniques to circumvent these obstacles. Through comprehensive evaluations, all three TSA techniques under investigation demonstrated significant potential in suppressing UAV-induced noise and minimising the effects of signal dropout. Traditional TSA showcased a remarkable sixfold enhancement in signal quality when analysed via the mean square error. However, the study also highlighted that while TSA and Multi-Cycle Time Synchronous Average (MCTSA) elevated signal clarity, there is a trade-off between noise suppression and signal duration. Additionally, the findings emphasise the importance of synchronisation between scanning and target vibration. To achieve optimal results in Continuous Scanning Laser Doppler Vibrometer measurements, there is a need for advanced algorithms capable of estimating target vibration and synchronising scanning in real-time. As the study was rooted in steady-state vibrations, future research should explore transient vibration scenarios, thereby broadening the application scope of TSA techniques in UAV-mounted LDV systems.

A differential diagnosis approach of localized planet gear fault using overall transmission error

Owing to recent studies on the relationship between the transmission error and the localized gear fault, TE-based differential diagnosis (DD) technology has emerged as a promising approach to identifying the tooth flank spall and the gear fillet crack which have similar signatures but quite different fault mechanisms and prognoses. However, due to complex structural factors, the fault modes can hardly be identified from the calculated overall transmission error (OTE). In light of this, a novel DD framework based on encoder-measured data is proposed in this work. Specifically, the OTE signal calculated from resampled data are filtered by flexible time synchronous average. A reordered singular values decomposition (RSVD) approach guided by the sparse index L2/L1 norm is then developed for fault-dependent residual OTE extraction. Experimental results validate that the faulty events can be accurately identified by the proposed framework without prior information under different speed conditions for planet gear DD.

Model-based prediction of otoacoustic emission level, noise level, and signal-to-noise ratio during time-synchronous averaging.

Although averaging is effective in reducing noise, its efficiency rapidly decreases beyond several hundred averages. Depending on environmental and patient noise levels, several hundred averages may be insufficient for informed clinical decision making. The predictable nature of the otoacoustic emission (OAE) and noise during time-synchronous averaging implicates the use of predictive modeling as an alternative to increased averaging when noise is high. Click-evoked OAEs were measured in 98, normal-hearing subjects. Average OAE and noise levels were calculated for subsets of the total number of averages and then fit using variants of a power function. The accuracy of the models was quantified as the difference between the measured value and model output. Models were used to predict the OAE signal-to-noise ratio (SNR) for a criterion noise level. Based on predictions, the OAE was categorized as present or absent. Model-based decisions were compared to decisions from direct measurements. Model accuracy improved as the number of averages (and SNR in the case of OAEs) from which the model was derived increased. Model-based classifications permitted correct categorization of the OAE status from fewer averages than measurement-based classifications. Furthermore, model-based predictions resulted in fewer false positives (i.e., absent OAE despite normal hearing).

Period-refined CYCBD using time synchronous averaging for the feature extraction of bearing fault under heavy noise

Deconvolution methods have been widely used in machinery fault diagnosis. However, their application would be confined due to the heavy noise and complex interference since the fault feature in the measured signal becomes rather weak. Time synchronous averaging (TSA) can enhance the periodic components and suppress the others by the comb filter function. And in the iteration process of the deconvolution methods, the filtered signal after each iteration can be further processed using TSA, and the time delay with maximum Gini index value is refined as the iterative period for the next iteration. Benefitting from these advantages, a period-refined maximum second-order cyclostationarity blind deconvolution (PRCYCBD) using TSA is proposed for the weak fault detection of rolling element bearings (REBs) in this paper. Firstly, without any prior knowledge, the proposed method which can estimate the period more accurately is more suitable for the weak fault detection of REBs, especially incipient fault. Secondly, TSA is firstly applied to estimate the iterative period rather than just depending on the Signal Noise Ratio (SNR) of the filtered signal in the iterative process . Furthermore, the new improvement frame can be expanded to other deconvolution methods using iterative algorithms, especially under heavy noise. Finally, a simulation with a slight bearing fault as well as two real experimental data including the vibration signal with the wind turbine bearing fault and the acoustical signal with the locomotive wheel bearing fault is used to verify the superiority of the proposed PRCYCBD compared with the traditional minimum entropy deconvolution and the traditional autocorrelation-improved cyclostationarity blind deconvolution.

Time Synchronous Averaging Based on Cross-power Spectrum

Periodic components are of great significance for fault diagnosis and health monitoring of rotating machinery. Time synchronous averaging is an effective and convenient technique for extracting those components. However, the performance of time synchronous averaging is seriously limited when the separate segments are poorly synchronized. This paper proposes a new averaging method capable of extracting periodic components without external reference and an accurate period to solve this problem. With this approach, phase detection and compensation eliminate all segments' phase differences, which enables the segments to be well synchronized. The effectiveness of the proposed method is validated by numerical and experimental signals.

Conditional Predictive Maintenance of Electric Vehicles from Electrical and Mechanical Faults

Maintenance of the core components of an electric vehicle is very crucial to ensure productivity, longevity, drive quality, and a safe environment. Predictive Maintenance is an approach that uses the operating & faulty condition data to predict future machine conditions and make decisions upon this prediction. The methodology used for predictive maintenance and condition monitoring can be based on machine learning and data analytics. The process of learning starts with the observation of data and using it in future instances for building the model. The primary aim is to allow the computer to learn without the involvement of the intervention of human assistance. A few machine learning methods are supervised learning, semi-supervised learning, and reinforcement learning. The main aim of the presented research is to use the available sensor data of the electric vehicle from various electronic control units and design a predictive model which classifies the various electrical and mechanical faults that occur in an electric vehicle and predicts the types for increasing the reliability of the whole electrical vehicular system. The workflow of the project is defined as fault modelling, generating healthy and fault data, processing the data using time synchronous averaging, identification of the system condition indicators and finally using these condition indicators, an SVM classification prediction model is designed from which the desired results and conclusion are inferred from the simulation studies.

Multi-resolution dynamic mode decomposition for damage detection in wind turbine gearboxes

Abstract We introduce an approach for damage detection in gearboxes based on the analysis of sensor data with the multi-resolution dynamic mode decomposition (mrDMD). The application focus is the condition monitoring of wind turbine gearboxes under varying load conditions, in particular irregular and stochastic wind fluctuations. We analyze data stemming from a simulated vibration response of a simple nonlinear gearbox model in a healthy and damaged scenario and under different wind conditions. With mrDMD applied on time-delay snapshots of the sensor data, we can extract components in these vibration signals that highlight features related to damage and enable its identification. A comparison with Fourier analysis, time synchronous averaging, and empirical mode decomposition shows the advantages of the proposed mrDMD-based data analysis approach for damage detection.

Detecting nicked automotive transmission gears in the final inspection by multi-scale time synchronous averaging

Detecting nicked automotive transmission gears in the final inspection by multi-scale time synchronous averaging

Towards real time automated early gear failure detection

The ability to stop a gear fatigue test before catastrophic failure has many advantages. However, today, a widely accepted approach is not available. This case study applies a vibration-based condition monitoring methodology to detect early gear failures. The gear studied takes part in an all-wheel-drive drivetrain system. Vibration signals from four run-to-failure fatigue tests at two constant torque-speed combinations were used as input to time-synchronous averaging and autoregression model generation. The applied methodology shows promising results for early failure detection, and the process is feasible for implementation in an automated environment. Real time analysis is also possible since the autoregression model generates a healthy state TSA signal during the early testing stages. However, the time to failure detection varies with operating conditions, with low sensitivity at high-speed and low-torque conditions.

Analysis of vibration signals and detection for multiple tooth cracks in spur gearboxes

Tooth crack diagnosis is important to ensure the reliability of gearboxes. However, most reported studies on tooth crack diagnosis only involved the scenario of one tooth crack in a gearbox, which is not always the case since gearboxes may also suffer from multiple tooth cracks in the industry. To overcome this problem, this study first brings some insights into the vibration characteristics of a spur gearbox with multiple tooth cracks using dynamic modelling. Three scenarios of multiple tooth cracks are considered, including two nonadjacent tooth cracks on the pinion and a healthy gear, two adjacent tooth cracks on the pinion and a healthy gear, and one tooth crack on the pinion and one tooth crack on the gear. Tooth mesh stiffness is analytically evaluated using the potential energy method and is further inserted into the spur gearbox dynamic model to generate vibration responses. Analyses of mesh stiffness and vibration responses are conducted in both time and frequency domains. Besides, a novel method focusing on the crack induced impulses (CII) is proposed to detect the number and locations of multiple tooth cracks. Firstly, the CII are extracted from gearbox vibration signals using a new strategy based on the singular value decomposition. Time synchronous average (TSA) is conducted on the CII to get the TSA signals for both the pinion and the gear. Afterwards, detection of the number and locations of multiple tooth cracks is achieved via analyzing the squared envelopes of the TSA signals for both the pinion and the gear. The obtained insights into vibration characteristics for multiple tooth cracks and the effectiveness of the proposed method for detecting the number and locations of multiple tooth cracks are demonstrated using both simulated gearbox vibration signals and experimental gearbox vibration datasets.