Vibrations In Rotating Machines Research Articles

A fault diagnosis network based on domain adversarial learning and distribution matching for rotating machine vibration signal with noise and across-load conditions

A fault diagnosis network based on domain adversarial learning and distribution matching for rotating machine vibration signal with noise and across-load conditions

Vibration Image Representations for Fault Diagnosis of Rotating Machines: A Review

Rotating machine vibration signals typically represent a large collection of responses from various sources in a machine, along with some background noise. This makes it challenging to precisely utilise the collected vibration signals for machine fault diagnosis. Much of the research in this area has focused on computing certain features of the original vibration signal in the time domain, frequency domain, and time–frequency domain, which can sufficiently describe the signal in essence. Yet, computing useful features from noisy fault signals, including measurement errors, needs expert prior knowledge and human labour. The past two decades have seen rapid developments in the application of feature-learning or representation-learning techniques that can automatically learn representations of time series vibration datasets to address this problem. These include supervised learning techniques with known data classes and unsupervised learning or clustering techniques with data classes or class boundaries that are not obtainable. More recent developments in the field of computer vision have led to a renewed interest in transforming the 1D time series vibration signal into a 2D image, which can often offer discriminative descriptions of vibration signals. Several forms of features can be learned from the vibration images, including shape, colour, texture, pixel intensity, etc. Given its high performance in fault diagnosis, the image representation of vibration signals is receiving growing attention from researchers. In this paper, we review the works associated with vibration image representation-based fault detection and diagnosis for rotating machines in order to chart the progress in this field. We present the first comprehensive survey of this topic by summarising and categorising existing vibration image representation techniques based on their characteristics and the processing domain of the vibration signal. In addition, we also analyse the application of these techniques in rotating machine fault detection and classification. Finally, we briefly outline future research directions based on the reviewed works.

Matching Synchroextracting Transform for Mechanical Fault Diagnosis Under Variable-Speed Conditions

Time–frequency (TF) analysis (TFA) technique has been widely used to the analysis of rotating machine vibration. However, vibration signal from practical sources contains complicated components and noise, so the fault diagnosis to variable-speed machinery is full of challenges. In this study, inspired by the demodulated synchrosqueezing transform (DSST), the synchroextracting transform (SET) is extended to a prior instantaneous frequency (IF) based method, named matching synchroextracting transform (MSET). To achieve the fault diagnosis using MSET, the follow-up works mainly include two parts. First, a demodulation filtering strategy is developed for multicomponent signal separation. Second, the order analysis based multiple IFs estimation idea and the second-order difference operator based IF smoothing scheme are introduced to obtain the reliable initial IFs. The effectiveness of the proposed technique is verified via some simulation studies. Finally, the proposed technique is successfully applied to the fault diagnosis of rolling bearing and planetary gearbox.

Enhancement of time-frequency post-processing readability for nonstationary signal analysis of rotating machinery: Principle and validation

Rotating machinery signals are often composed of multiple components and are nonstationary under practical time-varying conditions. In most cases, the constituent frequency components are close to each other on time–frequency plane. As such, better time–frequency readability is necessary to discover the underlying physical nature of such complex nonstationary signals. Nevertheless, conventional time–frequency analysis methods suffer from limited time–frequency resolution and/or cross-term interferences, thus cannot achieve desirable time–frequency readability. In recent decades, some time–frequency post-processing methods (such as time–frequency reassignment, synchrosqueezing transform, multi-tapering, concentration of frequency and time, and higher-order synchrosqueezing transform) have been proposed to improve the time–frequency readability from two aspects: reducing cross-term interferences and enhancing time–frequency energy concentration. However, they still surfer from time-frequency blurs for the signal whose time–frequency ridges are close to each other on time–frequency plane. To address this issue, we propose a framework to improve the post-processing methods. Firstly, the Vold-Kalman filter is employed to extract constituent frequency components, by exploiting its capability to separate mono-components. Then, the time–frequency representation (TFR) of each mono-component is obtained via time–frequency post-processing method separately. Finally, the TFR of raw signal is constructed by superposing the TFR of all mono-components. This framework achieves a TFR of high time–frequency resolution, free from cross-term interferences, and therefore better time–frequency readability. As such, it extends time–frequency post-processing methods to analyze complex nonstationary signals. The proposed framework is illustrated through numerical simulation, and validated using typical rotating machine vibration data (including the experimental signals of a planetary gearbox and an induction motor, ground test data of a civil aircraft engine and in-situ measurement of a hydraulic turbine rotor). The analysis results show the good capability of proposed framework to reveal the frequency contents and their time-varying features of complex nonstationary signals.

Low-cost MEMS accelerometer network for rotating machine vibration diagnostics

In this paper a wireless sensor network (WSN) with low-cost nodes (less than $US 30.00) is presented. The nodes are based on microelectromechanical systems (MEMS) accelerometers and a highly-integrated microcontroller with built-in antenna for Wi-Fi and Bluetooth Low-Energy (BLE). The system was specially designed to analyze unbalance, load, and rotor obstructions in a rotating machine. Two sensor nodes were installed on an apparatus: one in the shaft and the other on the support table. The acceleration signals were used to analyze the machine vibration in the frequency domain by its Fast Fourier Transform (FFT). This spectrum was pre-analyzed on the sensor node, and the most significant features were sent to a cloud platform. These nodes were mounted to acquire vibration from an electric bicycle motor. Three situations were simulated: unbalance, using weights at the endings of rim spokes; mechanical load, using neodymium magnets, through the principle of eddy currents; and rotor obstruction, using an object made with a nylon cable. The solution presented was used to measure vibration and calculate its spectrum and send it to the cloud. The signals were analyzed using three strategies: an FFT amplitude level comparison, logistic regression, and neural network (NN). The analyses were carried out using the signals of each sensor independently and as a sensor network, this last showing better results. It was possible to diagnose each type of fault inserted in the tests, proving that the device developed can be used in industries as a low-cost alternative to monitor the health of rotating machines.

Online vibration monitoring system for rotating machinery based on 3-axis MEMS accelerometer

This article discusses the design of a rotating machine vibration monitoring system in real time by utilizing a 3-axis accelerometer MEMS sensor. The system consists of 2 main parts, namely, vibration data acquisition system and data analyse system with the principle of wireless data communication. The results of vibration measurements from each three MEMS accelerometer sensors will be forwarded by the Arduino Nano microcontroller to the data collection device, the Arduino Uno microcontroller. Also, the machine rotating speed data acquired by the TCRT5000 speed sensor will be forwarded by the Arduino Pro Mini microcontroller to the same collection device. Data that has been recorded by the collection device is then transmitted to the computer through wireless communication using the Node-MCU device. The Lab View software is used as a machine vibration data display that has previously been processed by the computer in accordance with parameters desired by the user. Testing stability of data transmission with a certain length of time and communication distance is carried out to ensure the measurement results are in accordance with the real-time vibration conditions. Test results show an average delay time of below 200ms for the farthest distance from the wireless device signal range.

Periodic fault signal enhancement in rotating machine vibrations via stochastic resonance

This paper proposes a novel approach to periodic fault signal enhancement in rotating machine vibrations with a tristable mechanical vibration amplifier (TMVA) by exploiting stochastic resonance (SR). The TMVA is a nonlinear physical structure system that consists of a cantilever beam and a magnet system. Through the TMVA, the periodic weak signal can be amplified with the assistance of noise in the regime of SR. Benefitting from a wider interwell spacing and a smoother potential curve, the TMVA produces a more regular output waveform with lower noise in a wider operating bandwidth as compared to the monostable and bistable amplifiers. Different from the traditional signal enhancement approach which is based on digital signal processing (DSP) techniques, the designed physical structure can realize signal enhancement in a simple, intuitive, effective and adaptive way without too much complex operations. The effectiveness and efficiency of the proposed approach are validated by a simulated fault signal and the practical bearing and gearbox fault signals, in comparison with a traditional DSP-based SR method. The principle of the proposed approach shows potential applications on rotating machine fault diagnosis area and other areas related to weak periodic signal enhancement.

Envelope analysis of rotating machine vibrations in variable speed conditions: A comprehensive treatment

Nowadays, the vibration analysis of rotating machine signals is a well-established methodology, rooted on powerful tools offered, in particular, by the theory of cyclostationary (CS) processes. Among them, the squared envelope spectrum (SES) is probably the most popular to detect random CS components which are typical symptoms, for instance, of rolling element bearing faults. Recent researches are shifted towards the extension of existing CS tools – originally devised in constant speed conditions – to the case of variable speed conditions. Many of these works combine the SES with computed order tracking after some preprocessing steps. The principal object of this paper is to organize these dispersed researches into a structured comprehensive framework. Three original features are furnished. First, a model of rotating machine signals is introduced which sheds light on the various components to be expected in the SES. Second, a critical comparison is made of three sophisticated methods, namely, the improved synchronous average, the cepstrum prewhitening, and the generalized synchronous average, used for suppressing the deterministic part. Also, a general envelope enhancement methodology which combines the latter two techniques with a time-domain filtering operation is revisited. All theoretical findings are experimentally validated on simulated and real-world vibration signals.

Time-frequency methods for signal analysis in wind turbines

Since wind turbines became one of the most often source of renewable energy, appropriate health and condition monitoring systems are required. Especially proper monitoring of offshore plants is very significant because the accessibility is difficult and inspections are very costly. In comparison with conventional rotating machine vibration monitoring, where steady conditions and stationary signal are usually assumed, the wind turbines are characterized by unsteady conditions due to variable rotational speed. Hence the vibration signal is non-stationary and interpretation of signal signatures may be more complex. The common approach to analyze such non-stationary signals is the use of a time-frequency method, usually Short-Time Fourier Transform, which is the most popular one due to its simplicity. Nevertheless, there are other methods which can give a different view at the analyzed data and provide new information. This article investigates the potential use of some other time-frequency methods, namely Wavelet Transform, Wigner-Ville distribution and Hilbert-Huang transform in wind plants monitoring systems and apply these methods to real measured data with additional simulated bearing fault signal. Finally, the mentioned methods are compared based on computational complexity, readability and interpretability. Though the last two criteria are very subjective, Short-Time Fourier Transform was finally chosen as the most effective method followed by Wavelet Transform.

Impact of the non-uniform angular sampling on mechanical signals

Angular sampling is a useful tool to study cyclic rotating machine vibrations. It can be implemented by using computer order tracking, directly, or by extracting position information from an acceleration signal. Instantaneous speed can also be angularly sampled using a counter technique. Angular sampling is sensitive to hardware imperfections (optical encoder precision, electrical perturbation, etc.). Angular sampled signal quantification step or sampling frequency determination is not identical to time domain. Angular sampling is also not adapted to study time domain signals like impulse response. Some of these imperfections can be viewed as non-uniform sampling.Non-uniform sampling consists in introducing a jitter in an angular sampling position. This jitter is random and different for every position. This jitter can be caused physically by some inaccuracies in position sensor measurements. Sampling a time domain relative signal against the angle can also be viewed as a non-uniform sampling. In this case the jitter is caused by speed fluctuation (constant speed case). Non-uniform sampling introduces an additional noise (that can be cyclostationary at order 2 if the original signal is cyclostationary at order 1). Non-uniform sampling also acts as a low pass filter. Its cut-off frequency depends on jitter standard deviation.An experimental bench was built. This bench uses a synchronous data acquisition board, an arbitrary generator, and a non-uniform clock generator. Experimentations are performed using various jitter values (from 0.1% to 20%) and are corroborated by previous theoretical studies.

用于光电仪器和相关测量的自混合干涉技术(特邀)

This paper reviews the development of a new configuration of interferometry, Self-mixing Interferometry(SMI). SMI is attractive because it doesn't require any optical part external to the laser and can be employed in a variety of measurements, ranging from the traditional measurements related to optical path length like displacements, small-amplitude vibrations, velocity; to sensing of weak optical echoes, like return loss and isolation factor measurements; and also to a special feature for the interaction with the medium, measurements of physical parameters, like laser line width, coherence length, and the alfa factor. Because it is also a coherent detection scheme, the SMI operation is close to the quantum limit of received field and the minimum detectable amplitudes of 20 pm/√Hz or better are currently achieved upon operation on diffusive targets, whereas a corner cube allows half-wavelength counting mode or 0.5 μm resolution on a dynamic range up to 2 m. SMI has a compact setup, and is easy to deploy in the field and can interface a variety of experiments from MEMS testing to rotating machine vibration testing to pickup of biological motility as described in the paper.

Application of computed order tracking, Vold–Kalman filtering and EMD in rotating machine vibration

This paper presents a study on rotating machine vibration signals by using computed order tracking, Vold–Kalman filtering and intrinsic mode functions from the empirical mode decomposition method. Through the sequential use of intrinsic mode function and order tracking methods, both speed synchronous and non-synchronous vibrations that modulate orders in rotating machine vibrations are distinguished, which is difficult when using each of the techniques in isolation alone. Simulation and experimental studies demonstrate the ability of extracting vibrations that modulate order signals through combining the techniques.

A model for three-dimensional simulation of acoustic emissions from rotating machine vibration

Industrial noise can be successfully mitigated with the combined use of passive and active noise control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. Control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional vibration-acoustic model is proposed in this paper. The signal of an accelerometer attached to the bulk of a centrifugal pump installed in an empty room was used as the input of this model. The signal of a microphone that changes its position in a spatial grid inside this room is the output. In the first stage, the ARX models are used to describe a SISO system where the input is the machine vibration and the output is the noise level measured at a certain point. In the second stage, spatial interpolation is used to estimate the model parameters. Results show good agreement between experimental data and model predictions, indicating the potential of using the model for the design of ANC.

Blind Separation of rotating machine signals using Penalized Mutual Information criterion and Minimal Distortion Principle

The Blind Separation problem of convolutive mixtures is addressed in this paper. We have developed a new algorithm based on a penalized mutual information criterion recently introduced in [El Rhabi et al., A penalized mutual information criterion for blind separation of convolutive mixtures, Signal Processing 84 (2004) 1979–1984] and which also allows to choose an optimal separator among an infinite number of valid separators that can extract the source signals in a certain sense according to the Minimal Distortion Principle. So, the minimisation of this criterion is easily done using a direct gradient approach without constraint on the displacements. Thus, our approach allows to restore directly the contribution of the sources to the sensor signals without post-processing as it is usually done. Finally, we illustrate the performances of our algorithm through simulations and on real rotating machine vibration signals.

Principal component analysis and blind source separation of modulated sources for electro-mechanical systems diagnostic

Under the only hypothesis of independent sources, blind source separation (BSS) consists of recovering these sources from several observed mixtures of them. As it extracts the contributions of the sources independently of the propagation medium, this approach is usually used when it is too difficult to modelise the transfer from the sources to the sensors. In that way, BSS is a promising tool for non-destructive machine condition monitoring by vibration analysis. Principal component analysis (PCA) is applied as a first step in the separation procedure to filter out the noise and whiten the observations. The crucial point in PCA and BSS methods remains that the observations are generally assumed to be noise-free or corrupted with spatially white noises. However, vibration signals issued from electro-mechanical systems as rotating machine vibration may be severely corrupted with spatially correlated noises and therefore the signal subspace will not be correctly estimated with PCA. This paper extends a robust-to-noise technique earlier developed for the separation of rotating machine signals. It exploited spectral matrices of delayed observations to eliminate the noise influence. In this paper, we focus on the modulated sources and prove that the proposed PCA is available to denoise such sources as well as sinusoidal ones. Finally, performance of the algorithm is investigated with experimental vibration data issued from a complex electro-mechanical system.

Diagnostic significance of orbit shape analysis and its application to improve machine fault detection

The full spectrum analysis of rotating machine vibrations is a diagnostic tool that enables the symptoms of some special types of fault to be clearly detected. The Shape and Directivity Index (SDI) of journal filtered orbits is an additional diagnostic parameter whose evaluation can be combined with the full spectrum analysis. The ellipticity of the filtered orbit, as well as the amplitude and the inclination angle of the major axis of the orbit, are parameters whose analysis can provide important diagnostic information. In order to validate the proposed approach, the vibrations of a large turbine-generator unit that was subjected to rotor-to-stator rubs have been analyzed in this paper. The results of this investigation have been used to update the model of the rotor-system that has been used to identify the location and the severity of the fault. In the paper, the improvements in the accuracy of the fault identification provided by the model updating enabled by the SDI analysis are shown.

Cyclostationary modelling of rotating machine vibration signals

This paper is dedicated to modelling rotating machine signals as cyclostationary processes, with strong emphasis on the peculiarities and pitfalls that this issue implies. The objective is to demonstrate that machine signals require specific processing that are much more subtle than for communication signals—from which the paradigm of cyclostationarity was originally issued from and developed for. First, different types of cyclostationarity embracing a multitude of rotating machine signals are distinguished. In particular, the importance of considering pure rather than impure cyclostationarity is stressed out. Next, the relationships between angle and time cyclostationarity are investigated and some useful results are derived. It is shown that vibration signals exhibit cyclostationarity if and only if the random speed fluctuation of the machine is periodic, stationary or cyclostationary. Finally, a comprehensive methodology is proposed for processing actual cyclostationary signals: three typical examples dealing with vibration signals of an IC engine, a gearbox and a rolling element bearing are presented, each of them being characterised by a different type of cyclostationarity. The methodology proposed in this paper is general enough and may serve as a guideline for modelling and analysing other types of rotating machine signals, such as pressure signals, electric signals, etc.

Blind source separation of noisy harmonic signals for rotating machine diagnosis

Blind source separation (BSS) consists of recovering signals from different physical sources from several observed combinations independently of the propagation medium. BSS is also a promising tool for non-destructive machine condition monitoring by vibration analysis, as it is intended to retrieve the signature of a single rotating machine from combinations of several working machines. In this way, BSS can be seen as a pre-processing step that improves the diagnosis. BSS methods generally assume observations that are either noise-free or corrupted with spatially distinct white noises. In the latter case, principal component analysis (PCA) is applied as a first step to filter out the noise and whiten the observations. Obviously, the efficiency of the whole separation procedure depends on the accuracy of the first step (PCA). However, in the real world, signals of rotating machine vibration may be severely corrupted with spatially correlated noises and therefore the signal subspace will not be correctly estimated with PCA. The purpose of this paper is to propose a ‘robust-to-noise’ technique for the separation of rotating machine signals. The sources are assumed here to be periodic and so can be modelled as the sum of sinusoids of harmonic frequencies. A new estimator of the signal subspace and the whitening matrix is introduced which exploits the model of sinusoidal sources and uses spectral matrices of delayed observations to eliminate the influence of the noise. After whitening, the second step of source separation remains unchanged. Finally, performance of the algorithm is investigated with artificial data and experimental rotating machine vibration data.

Application of periodic time-varying autoregressive models to the detection of bearing faults

Model-based approaches to vibration monitoring can provide a means of detecting machine faults even if data are only available from the machine in its normal condition. The cyclostationary nature of rotating machine vibrations can be exploited by using periodic time-varying autoregressive models to model the signal better than time-invariant models. Experimental data collected from a small rotating machine set subjected to several bearing faults were used to compare time-varying and time-invariant models. Comparison is also made with a simple feature-based neural network fault diagnosis system.

Applications of knowledge-based programming to power engineering problems

Application of knowledge-based expert systems to typical power engineering problems involving diagnosis, control and design is described. A review of prototype projects completed at North Carolina State University serves to demonstrate what is involved in expert system development. The examples include a diagnostic tool for rotating-machine vibration problems, implementation of a distribution-feeder reconfiguration control scheme, and a design aid for distribution-feeder capacitor placement. >