Self-adaptive Noise Cancellation Research Articles

A practical methodology for enhancement and detection of transient faults in a gearbox without prior fault feature information

Gear fault diagnosis has been the focus of research in both academia and industry in the past. Due to the complicated structures of gearboxes, the vibration signals collected from the gearbox casings are comprised of multiple sources, in which the fault-related components may be easily masked by the Gaussian and non-Gaussian noises (e.g. random external shocks and gear meshing harmonics). Moreover, fault feature frequencies of interest cannot always be obtained in advance in practical applications. Therefore, it is necessary to choose an appropriate signal processing method for gear fault diagnosis. In this study, a new practical methodology is proposed to extract the transient fault features from a gearbox vibration signal corrupted by complicated interference without prior fault feature information. In the proposed method, a modified self-adaptive noise cancellation (MSANC) algorithm is first developed for removing the interferences of gear meshing harmonics adaptively, which can overcome the shortcomings of the traditional SANC in parameter selection and convergence performance. Based on the de-noised signal, a cyclic spectral analysis tool called the fast spectral correlation and assisted by the multipoint optimal minimum entropy deconvolution adjusted algorithm is then applied to enhance and detect the certain and potential gear faults. The effectiveness of the proposed method is demonstrated by a numerical simulation and two experimental scenarios that are compared to an advanced blind optimal demodulation band selection technique. The results reveal that the proposed methodology has good capability to detect mono- and multiple gear faults under complicated interferences, and can be regarded as an efficient method for practical gear fault diagnosis, especially for applications where the fault feature frequencies are unknown in advance.

A Novel LMS-Based SANC for Conveyor Belt-Type Checkweigher

The conveyor belt-type checkweighers are increasingly employed for dynamic weighing in the mass production line, such as agricultural products, chemical, pharmaceutical, and other manufacturing industries. However, this dynamic weighing system is easily influenced by the environmental and mechanical vibrations, which can disrupt the weighing results accuracy and the weighing throughput rate. Besides, the checkweigher systems are complex and time-varying. Therefore, an adaptive filter called self-adaptive noise cancellation (SANC) is presented in this article to eliminate the impaction of these vibrations, and a novel variable step-size least mean-squares (NVS-LMS) algorithm is proposed. Finally, a checkweigher system, with 24-bit ADC and a processor of STM32F405, to evaluate the performance of the approach in-field was constructed. The results show that the mean errors and the standard deviations of the error about the tests can meet the requirements of category XIII (0.1) in OIML R 51, and the system can operate in the speed up to 1.75 m/s.

Fractional Fourier Transform Based Adaptive Filtering Techniques for Acoustic Emission Signal Enhancement

Acoustic emission technique (AET) is a well-known non-destructive testing method used for the detection of crack growth and monitoring structural integrity of components. The main limitation in the application of AET is the fact that acoustic emission (AE) signal is often affected by background noise. A new approach is proposed in this paper to reduce the background noise and to improve the accuracy of AE signal detection. Various noise reduction methods have been applied and studied for AE signal enhancement so far. Some of the studies proved that self-adaptive noise cancellation (SANC) and adaptive noise cancellation (ANC) are better techniques for denoising the AE signal. This paper proposes the use of ANC and SANC schemes based on Fractional Fourier Transform (FrFT) for AE signal enhancement. FrFT is the generalization of the classical Fourier Transform (FT). The use of FrFT gives an advantage of an additional degree of freedom (angle of rotation in time–frequency plane) over the traditional Fourier transform and could provide improved performance. In this method, the noisy signal is rotated in time- frequency plane to extract the signal in Fractional Fourier domain (FrFD). Two adaptive filters viz. least mean squares and normalized least mean squares are studied for FrFD based ANC approach. The performance of the proposed method is validated using real AE signals acquired from three different experiments: pencil lead break test, composite drilling test and concrete compression test. Results are compared with the time and frequency domain adaptations. Moreover, the results have also been compared with the frequency domain adaptation techniques. Results have demonstrated that FrFD based ANC approach outperforms the conventional time domain and frequency domain adaptive filtering with an improved signal to noise ratio and significantly reduces mean square error.

Cyclostationary modeling for local fault diagnosis of planetary gear vibration signals

Owing to the rotation and reciprocation of mechanical equipment, their vibration signals inherently exhibit cyclic stationary characteristics. This paper provides a phenomenological signal model of the planetary gear vibrations under the cyclostationarity paradigm. The model is helpful to give comprehensive demodulation information which is the limit of classic spectral analysis of deterministic signals, so that the diagnosis of local faults in planetary gear sets can be more explicit. First, the effects of rotating speed fluctuation on periodic gear meshing vibrations are analyzed. It is shown that the stationary speed fluctuation will turn the periodicity of signals into second-order cyclostationarity. Next, an impulsive oscillation sequence with interval timing jitter is used to describe the dynamic response of the meshing stiffness change caused by local defects. Meanwhile, the influence of a vibration transfer path is also considered in the model. Based on the above analysis, the following procedure is utilized to analyze planetary gearbox vibrations: remove the periodic components by the self-adaptive noise cancellation method and then, apply the second-order frequency descriptor to reveal fault features of the signals. Both the simulation and experiment are used to validate the correctness of model derivation and the superiority of the cyclostationary analysis.

Using frequency domain analysis techniques for diagnosis of planetary bearing defect in a CH-46E helicopter aft gearbox

Abstract Condition monitoring for helicopters has always been one of the most critical technologies to guarantee the integrity of the rotorcrafts, enhance operational and personnel safety, and reduce the overall maintenance costs. Over the past decades, health and usage monitoring system (HUMS) has been developed and implemented in helicopters to monitor the health status for the main gearbox (MGB) and other key components of the transmission system, improving condition-based maintenance for helicopters. However, many studies have indicated that current HUMS has a limited sensitivity to MGB planetary bearing defects. To enhance HUMS' performance, this paper presents an approach based on frequency domain analysis techniques to diagnose planetary bearing defects using real helicopter data collected from a CH-46E helicopter aft MGB. Vibration data was processed using signal processing techniques including self-adaptive noise cancellation (SANC), discrete-random separation (DRS), cepstrum editing, kurtogram, envelope analysis and iterative envelope cancellation. Processing results conclude that frequency domain analysis techniques can provide distinct and intuitive indications of the seeded defects at both the inner race and the outer race of the faulty planetary bearing.

A study on helicopter main gearbox planetary bearing fault diagnosis

The condition monitoring of helicopter main gearbox (MGB) is crucial for operation safety, flight airworthiness and maintenance scheduling. Currently, the helicopter health and usage monitoring system, HUMS, is installed on helicopters to monitor the health state of their transmission systems and predict remaining useful life of key helicopter components. However, recent helicopter accidents related to MGB failures indicate that HUMS is not sensitive and accurate enough to diagnose MGB planetary bearing defects. To contribute in improving the diagnostic capability of HUMS, diagnosis of a MGB planetary bearing with seeded defect was investigated in this study. A commercial SA330 MGB was adopted for the seeded defect tests. Two test cases are demonstrated in this paper: the MGB at 16,000 rpm input speed with 180 kW load and at 23,000 rpm input speed with 1760 kW load. Vibration data was recorded, and processed using signal processing techniques including self-adaptive noise cancellation (SANC), kurtogram and envelope analysis. Processing results indicate that the seeded planetary bearing defect was successfully detected in both test cases.

Design of Background Noise Suppression Circuit for Mobile Phones

With the purpose of reducing the influence of background noise on the call quality of mobile phones, background noise suppression circuit is designed based on the principle of self-adaptive noise cancellation. Because this method is not involved in the nature of the noise itself, it can be used both for stationary noise cancellation and quasi-stationary noise cancellation. The working principle and circuit design of the system are introduced in detail. Simulated experiment was conducted in the lab, and its experimental results were analyzed. The experimental results show that the circuit works well with low cost, and has a broad prospect of application and popularization.

Planetary gearbox fault feature enhancement based on combined adaptive filter method

The reliability of vibration signals acquired from a planetary gear system (the indispensable part of wind turbine gearbox) is directly related to the accuracy of fault diagnosis. The complex operation environment leads to lots of interference signals which are included in the vibration signals. Furthermore, both multiple gears meshing with each other and the differences in transmission rout produce strong nonlinearity in the vibration signals, which makes it difficult to eliminate the noise. This article presents a combined adaptive filter method by taking a delayed signal as reference signal, the Self-Adaptive Noise Cancellation method is adopted to eliminate the white noise. In the meanwhile, by applying Gaussian function to transform the input signal into high-dimension feature-space signal, the kernel least mean square algorithm is used to cancel the nonlinear interference. Effectiveness of the method has been verified by simulation signals and test rig signals. By dealing with simulation signal, the signal-to-noise ratio can be improved around 30 dB (white noise) and the amplitude of nonlinear interference signal can be depressed up to 50%. Experimental results show remarkable improvements and enhance gear fault features.

A comparative study of adaptive filters in detecting a naturally degraded bearing within a gearbox

The diagnosis of bearing faults at the earliest stage is critical in avoiding future catastrophic failures. Many diagnostic techniques have been developed and applied in for such purposes, however, these traditional diagnostic techniques are not always successful when the bearing fault occurs within a gearbox where the vibration response is complex; under such circumstances it may be necessary to separate the bearing vibration signature.This paper presents a comparative study of four different techniques for bearing signature separation within a gearbox. The effectiveness of these individual techniques were compared in diagnosing a bearing defect within a gearbox employed for endurance tests of an aircraft control system. The techniques investigated include the least mean square (LMS), self-adaptive noise cancellation (SANC) and the fast block LMS (FBLMS). All three techniques were applied to measured vibration signals taken throughout the endurance test. In conclusion it is shown that the LMS technique detected the bearing fault earliest.

Application of Linear Prediction, Self-Adaptive Noise Cancellation and Spectral Kurtosis in Identifying Natural Damage of a Rolling Element Bearing in a Gearbox

Application of Linear Prediction, Self-Adaptive Noise Cancellation and Spectral Kurtosis in Identifying Natural Damage of a Rolling Element Bearing in a Gearbox

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.

Improving defect detection of rolling element bearings in the presence of external vibrations using adaptive noise cancellation and multiscale morphology

Identifying the signals of defective rolling element bearings of industrial machines in the presence of external vibrations is a difficult task. Therefore, in this article, attempt has been done to improve the defect detection of rolling element bearings in the presence of external vibrations using adaptive noise cancellation (ANC), self-adaptive noise cancellation (SANC), and mathematical multiscale morphology (MMM). Circular defects (diameter = 400 µm), on either of the races of the test bearings, have been artificially created and external random vibrations have been imparted to the defective test bearings using an electromechanical shaker in the experimentation reported herein. The defective bearing signal-to-external noise (vibration) ratio has been significantly enhanced after the application of ANC, SANC, and MMM. This resulted in the clear identification of defect frequencies in the vibration spectrum. It is essential to mention here that in ANC technique the least mean square (LMS) algorithm with and without signum function has been used. However, in MMM technique, triangular structural elements are utilized during the closing operation and bottom-hat transform (BHat). In comparison to the LMS algorithm without signum function, the LMS algorithm with signum function has been proved more effective and efficient in minimizing the error. Authors noticed that the MMM filter is much effective for noise removal.

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.

Effect of the Denoising on Acoustic Emission Signals

The acoustic emission (AE) technology is growing in its applicability to bearing defect diagnosis. Several publications have shown its effectiveness for earlier detection of bearing defects than vibration analysis. In the latter instance, detection and monitoring of defects can be achieved through temporal statistical indicators, which can further be improved by application of denoising techniques. This paper investigates the application of temporal statistical indicators for AE detection of bearing defects on a purposely built test-rig and assesses the effectiveness of various denoising techniques in improving sensitivity to early defect detection. It is concluded that the denoising methods offer significant improvements in identifying defects with AE, especially the self-adaptive noise cancellation method.

Enhanced Unsupervised Noise Cancellation using Angular Resampling for Planetary Bearing Fault Diagnosis

The bearings of planetary gears provide one of the most difficult scenarios for detection and diagnostics of bearing faults, since the fault signals must pass through a tortuous and time-varying path to arrive at external measurement points where they can be detected. In the case of helicopter gearboxes, this is made even more difficult by the fact that strong background masking signals exist over the full acoustic frequency range, in particular from gears which convert an input shaft frequency at gas turbine speed of typically 350 Hz to a rotor output speed of typically 5 Hz. The first step in analysing bearing signals is to remove the contributions from the gears. Classically, in normal gearboxes, this is done by using a simple band pass filter that exploits their different frequency ranges. However, with helicopter gearbox signals, it is usually necessary to first remove the masking signals from gears before the bearing signals can be analysed. In a previous article by Ho, the self-adaptive noise cancellation technique was used to improve envelope analysis results, but this requires the gear signals to be deterministic and phase-locked to shaft speeds. If the shaft speeds vary somewhat, it may be necessary to resample the signals on an angular rather than temporal basis (so-called order tracking), to force the gear signals to be deterministic. This normally requires the use of a shaft phase-locked tachometer signal to perform the angular resampling. The aim of this article is to show how the angular resampling can be performed from the signal itself without the requirement of a tachometer signal, thus combining unsupervised noise cancellation with angular resampling (i.e., effectively eliminating the speed fluctuation) to enhance the quality of the separation. A newly developed separation technique is utilised, which is much more efficient than that used by Ho. After presentation of the separation technique, its application to a particularly difficult diagnostic problem is then detailed.

Unsupervised noise cancellation for vibration signals: part II—a novel frequency-domain algorithm

Vibration analysis of machines gains greatly in efficiency if periodic vibrations can be separated out from non-deterministic ones. Part II of this paper introduces a solution in the frequency domain, which is faster and simpler to use than adapative algorithms (part I). The performance of this new algorithm is thoroughly investigated and compared to those of the self-adaptive noise cancellation algorithm. Finally, convincing examples are given of the application to the separation of actual bearing signals from gear signals in gearboxes.

Unsupervised noise cancellation for vibration signals: part I—evaluation of adaptive algorithms

Vibration analysis of machines gains much in efficiency if periodic vibrations can be separated out from non-deterministic ones. The first part of this paper reviews the existing adaptive algorithms that may be used to achieve this goal. The presentation is carried out from the viewpoint of prediction theory and gives a more solid theoretical basis to a number of recommendations for setting the algorithm parameters, as well as speeding up the computation by fast convolution using FFT processing. The application of Self-Adaptive Noise Cancellation is then illustrated on simulated and actual vibration signals. This first part is an introduction to part II of the same topic where a new method is proposed.

OPTIMISATION OF BEARING DIAGNOSTIC TECHNIQUES USING SIMULATED AND ACTUAL BEARING FAULT SIGNALS

In this paper, bearing fault vibrations are modelled as a series of impulse responses of a single-degree-of-freedom system. The model incorporates slight random variations in the time between pulses so as to resemble actual vibration signals. Although the bearing fault harmonics in the raw spectrum are caused by the random fluctuations to smear over one another, they remain quite clear in the spectrum of the envelope. However, the envelope spectrum is still prone to masking by discrete and random noise. Therefore, the simulated bearing fault signals were used to investigate the efficient application of self-adaptive noise cancellation (SANC) in conjunction with envelope analysis in order to remove discrete frequency masking signals. Two ways of combining these techniques have been suggested, both of which require the original signal to be band-pass filtered and frequency-shifted in order to reduce the number of samples to be processed by SANC. The subsequent envelope analysis can then be performed by using the Hilbert transform technique or band-pass rectification. Band-pass rectification is simpler but requires extra zero padding above and below the demodulation band, making the length of the signal processed by SANC twice as long as with the former method, but still only a fraction of the length of the original signal. On the other hand, the Hilbert technique requires an extra forward and inverse discrete Fourier transform operation compared with band-pass rectification. These two methods reduce the masking effects in the envelope spectrum by removing pseudo-sum frequencies or placing them outside the frequency range of interest. This is illustrated with examples of simulated and actual vibration signals. The removal of discrete frequency noise using SANC is also demonstrated for actual vibration signals. The threshold for which analysing the squared envelope or its higher powers gives an improvement in the envelope spectrum has also been defined using simulated and actual vibration signals. The treatment in the paper is qualitative and non-mathematical for purposes of clarity, but reference is made to a quantitative treatment of the effects of masking.

Bell helicopter textron

The condition monitoring of helicopter main gearbox (MGB) is crucial for operation safety, flight airworthiness and maintenance scheduling. Currently, the helicopter health and usage monitoring system, HUMS, is installed on helicopters to monitor the health state of their transmission systems and predict remaining useful life of key helicopter components. However, recent helicopter accidents related to MGB failures indicate that HUMS is not sensitive and accurate enough to diagnose MGB planetary bearing defects. To contribute in improving the diagnostic capability of HUMS, diagnosis of a MGB planetary bearing with seeded defect was investigated in this study. A commercial SA330 MGB was adopted for the seeded defect tests. Two test cases are demonstrated in this paper: the MGB at 16,000 rpm input speed with 180 kW load and at 23,000 rpm input speed with 1760 kW load. Vibration data was recorded, and processed using signal processing techniques including self-adaptive noise cancellation (SANC), kurtogram and envelope analysis. Processing results indicate that the seeded planetary bearing defect was successfully detected in both test cases.