Condition Monitoring Of Rotating Machinery Research Articles

Weighted squared envelope nonlinear Gini index and its application to rotating machinery condition monitoring

Abstract Sparsity measures are effective tools for rotating machinery condition monitoring. However, under complex operating conditions, existing sparsity measures often exhibit significant fluctuations, making it difficult to accurately detect early faults and monotonically represent the degradation processes. To tackle these challenges, a novel health indicator named the weighted squared envelope nonlinear Gini index is proposed in this paper. Firstly, by introducing a nonlinearly increasing weight sequence inspired by the sigmoid function and the quadratic function-based quasi-arithmetic mean, a novel sparsity measure, the nonlinear Gini index, is developed based on the ratio of different quasi-arithmetic means framework. Building upon this foundation, the weighted squared envelope nonlinear Gini index is further constructed for condition monitoring by incorporating the weighted squared envelope into the nonlinear Gini index. When applied to two bearing run-to-failure datasets, the proposed health indicator shows improved sensitivity to early fault features and is able to depict the degradation processes monotonically, demonstrating notable advantages in condition monitoring.

Horizontal rearrangement frequency domain chirplet transform: algorithm and applications

Abstract Based on the idea of optimal time–frequency analysis (TFA) theory, a time–frequency post-processing method called horizontal rearrangement frequency domain chirplet transform (HRFCT) is proposed. The proposed method is designed to analyze non-stationary transient signals with nonlinear group delay (GD), aiming at obtaining energy-concentrated and accurate time–frequency representation (TFR). First, based on the image characteristics of the constructed amplitude function, the selection criteria of GD are defined, that is, the local maximum of amplitude function and the local minimum of absolute value of derivative of amplitude function are calculated. Next, according to the calculated GD, the coefficients on the GD trajectory are adaptively extracted, and the coefficients that cause the TFR to be blurred are discarded. Finally, the analysis result of a two-component simulation signal demonstrates the satisfactory performance of the HRFCT, and compared with four advanced TFA methods, the competitiveness of the HRFCT is verified. Additionally, the application of the HRFCT in the processing of constant and variable speed bearing signals highlights its prospects in transient signal analysis and rotating machinery condition monitoring.

Development of Diagnosis System for Rolling Bearings Faults on Real Time Based on FPGA

The real-time monitoring of events in an industrial plant is an advanced technique that presents the real conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes various rotating machinery condition monitoring techniques of the machine to determine the conditions of failure. To increase the operational reliability and to reduce preventive maintenance, it is necessary an efficient tool for analysis and process monitoring, in real time, enabling the detection of incipient faults for rolling bearings. Over the past few years there has been a major technological developments related to digital system, including innovations in both hardware and software. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades and expansions of the designed system. This paper presents a study of new design tools for embedded digital systems based on open hardware architecture with reconfigurable logic. Will be discussed a case study in the area of fault detection of rolling bearings, as well as its implementation and testing.

Toward efficient and accurate extraction of instantaneous frequency with chirplet transform and its applications in bearing fault diagnosis

In many engineering applications, the instantaneous frequency (IF) needs to be identified rapidly and precisely, especially in rotating machinery condition monitoring, where quasi-real-time analysis is a favorable option, and any error in shaft speed (identified from the instantaneous frequency) needs to be reduced to prevent it from accumulating with time. The modern time–frequency analysis (TFA) method provides a way for IF identification; however, sometimes, it still suffers from energy smearing and closely spaced frequency interference. In addition, many TFA methods based on the chirplet transform also present the problem of excessive computation when searching for the optimal parameters. To overcome these limitations, we propose the multi-extracting velocity-synchronous chirplet transform (MEVCT), an enhanced TFA method, in this paper. In the MEVCT, a refined chirp parameter search is developed first to reduce the computational cost while increasing time–frequency accuracy. Then, an iterative high-order IF estimator is constructed to allow the IF to achieve or approach the actual values even in highly nonlinear situations. After that, the obtained IF estimate is substituted into the simultaneous extraction operator to reduce energy dispersion further and eliminate non-reassigned points (NRPs). Simulated and experimental signal analyses demonstrate that MEVCT is able to eliminate NRPs completely. Furthermore, when handling synchronous multicomponent signals, the proposed method has higher IF accuracy with less computational cost and better anti-noise capability.

Research on Rotating Machinery Fault Diagnosis Based on an Improved Eulerian Video Motion Magnification.

Rotating machinery condition monitoring and fault diagnosis are important bases for maintenance decisions, as the vibrations generated during operation are usually imperceptible to the naked eye. Eulerian video motion magnification (EVMM) can reveal subtle changes and has been widely used in various fields such as medicine, structural analysis, and fault diagnosis, etc. However, the method has a bound relationship among three parameters: spatial wavelength, amplification factor, and displacement function, so it is necessary to adjust the parameters manually in practical applications. In this paper, on the basis of the original method, an automatic solution of spatial cutoff wavelength based on brightness is proposed. First, an input video is decomposed into image sequences, their RGB color spaces are transformed into HSV color spaces, and the Value channel image representing brightness is selected to automatically calculate the spatial cutoff frequency, and then the spatial cutoff wavelength is determined, and the motion magnification video in the specified frequency band is obtained by substituting it into the original method. Then, a publicly available video is taken as an example for simulation analysis. By comparing the time-brightness curves of the three videos (original video, motion magnification video obtained by the original method and the improved method), it is apparent that the proposed method exhibits the most significant brightness variation. Finally, taking an overhung rotor-bearing test device as the object, five conditions are set, respectively: normal, rotor unbalance, loosened anchor bolt of the bearing seat, compound fault, rotor misalignment. The proposed method is adopted to magnify the motion of the characteristic frequency bands including 1X frequency and 2X frequency. The results show that no obvious displacement is found in normal working conditions, and that the rotor unbalance fault has an overall axial shaking, the bearing seat at the loose place has an obvious vertical displacement, while the compound fault combines the both fault characteristics, and the rotor misalignment fault has an obvious axial displacement of the free-end bearing seat. The method proposed in this paper can automatically obtain the space cutoff wavelength, which solves the problem of defects arising from manually adjusting the parameters in the original method, and provides a new method for rotating machinery fault diagnosis and other fields of application.

A novel method to repair missing vibration data in rolling bearing vibration signals based on improved optimized measurement matrix

Missing data, caused by many factors such as equipment short circuits or data cleaning, affect the accuracy of condition monitoring for rotating machinery. To improve the precision of missing data recovery, a compressed sensing-based vibration data repair method is developed. First, based on the Gaussian random matrix, an improved optimized measurement matrix (OMM) is proposed to accurately sample data. Then, a sparse representation of the vibration signal, through a discrete cosine transform, is utilized to make the noisy vibration signal sparse. Finally, the orthogonal matching pursuit algorithm is employed to reconstruct the missing signal. The effectiveness of the proposed method is verified by analyzing constant and variable speed time series of rolling bearings. Compared with other data repair methods, it is shown that the OMM has a higher repair precision at different loss rates.

A Reinforced Noise Resistant Correlation Method for Bearing Condition Monitoring

Condition monitoring plays a significant role in guaranteeing the reliability and safety of rotating machinery, which aims to detect an incipient fault and assess the degradation tendency. The construction of a health index (HI) is a crucial step to realize above tasks. At present, kurtosis, crest factor, and so on have been recognized as popular HIs to depict the operating condition. However, shortcomings of these classical HIs still exist: 1) classical HIs are prone to be affected by strong white Gaussian noise; 2) classical HIs are not sensitive to incipient faults. To deal with these two shortcomings, a reinforced noise resistant correlation method is proposed in this paper. Firstly, a new signal is constructed using the steps of segmenting and averaging to suppress the interference of noise. Then, a novel correlation function is used to get the hidden period. The proposed HI is constructed based on the discrete version of this correlation function to increase the sensitivity of incipient faults. Subsequently, theoretical values of the proposed HI under healthy states are investigated. The effectiveness of the method is demonstrated using simulated degradation processes and two accelerated degradation datasets of rolling element bearings. Through comparisons with other classical HIs, the proposed HI can simultaneously suppress the interference of strong noise and detect incipient faults. The comparison results identify the effectiveness of the proposed method in monitoring the condition of rotating machinery. Note to Practitioners—This work aims to provide a novel health index construction method for rotating machinery condition monitoring. The key issues involved in this problem include 1) how to capture the complex relationship between the measured vibration signals and the underlying health condition of rotating machinery; 2) how to suppress the interference of environmental noise. The novelty of this work is that it develops a method for condition monitoring considering the measured signals with strong white Gaussian noise. It properly captures the complex relationship between measured signals and the underlying health condition. There are four main steps to implement this approach: 1) collecting the vibration signals from rotating machinery; 2) constructing new periodic signal to suppress the interference of strong background noise; 3) constructing novel correlation functions to establish the relationship between the vibration signals and the underlying health condition of the rotating machine; 4) modeling the HI. A simulation and two real cases of the bearing degradation process are used to show that the proposed HI can simultaneously suppress the interference of strong noise and detect the incipient faults compared with some classical HI construction methods. In the future, the problem of how to address the measured signals contaminated by non-Gaussian noise and the machinery containing compound faults should be solved to extend this method in a complex system.

A Blind Deconvolution Approach Based on Spectral Harmonics-to-Noise Ratio for Rotating Machinery Condition Monitoring

Harmonics-to-noise ratio (HNR) is an important health index of rotating machine, which has been applied in blind deconvolution (BD) method to realize periodic impulse detection. However, most fault impulses are not strictly periodic, but pseudo-cyclostationary, which will affect the performance of HNR in fault characterization to some extent. This limits its applications. Therefore, in this paper, a novel BD method, maximum squared envelope spectrum harmonic-to-interference ratio deconvolution (MSESHIRD), is proposed to more effectively achieve fault identification. The proposed method seeks a target filter by maximizing squared envelope spectrum harmonic-to-interference ratio (SESHIR). Since harmonic components corresponding to repetitive fault impulses in SES are less sensitive to random fluctuations, SESHIR can more accurately distinguish repetitive fault impulses from irrelevant interference in vibration signals. Therefore, BD based on SESHIR has better performance than BD based on HNR in measuring fault features in signals. Through simulation and experimental case analysis, the proposed method is compared with several public methods Results show that the proposed method has better performance in fault characteristic extraction. In addition, it is implemented on bearing run-to-failure data for condition monitoring to show that the proposed method has excellent ability of early fault detection. Note to Practitioners—This paper is motivated by the problems of automatic operating condition monitoring and early defect diagnosis of rotating machines. These problems can be effectively solved by designing a BD method based on reliable and efficient health indices. HNR defined on autocorrelation function (AF) is an excellent health index to characterize the signal-to-noise ratio (SNR) of repetitive fault impulse in signals. However, this paper uses mathematical models of HNR to show that it has very strict requirements on the period and SNR of fault impulse signals. A fluctuating fault period or a low SNR might make HNR unable to accurately estimate the energy of fault components in signals, thus weakening its performance in fault characterization. Compared with HNR, SESHIR has better fault characterization ability due to that SES can more accurately obtain the periodicity (frequency) and energy of fault components in signals. Therefore, this paper proposes a novel BD method based on SESHIR maximization for repetitive impulse monitoring. Its effectiveness and robustness are verified by both theoretical justification and experimental results.

Proportion-Extracting Chirplet Transform for Nonstationary Signal Analysis of Rotating Machinery

Multicomponent nonstationary signals with close instantaneous frequencies (IFs) are commonly encountered in rotating machinery condition monitoring and fault diagnosis. It is very challenging to accurately reveal the physical natures of such signals. To address the issue, this article presents the proportion-extracting chirplet transform (PECT). In the PECT, the proportional kernel functions can match with the time-varying patterns of all constituent components. This enables the PECT to eliminate the artifacts caused by spectral overlaps in short-time windows and to achieve fine frequency resolution. Meanwhile, the corresponding proportional frequency shifting operators ensure satisfactory time resolution. Therefore, the PECT provides high time-frequency resolution and suffices to characterize the time–frequency structures of nonstationary signals with close IFs. The experimental and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measured data analysis results of typical rotating machinery validate the effectiveness of the PECT.

Hybrid energy harvesting for self-powered rotor condition monitoring using maximal utilization strategy in structural space and operation process

The energy harvesting technology is capable of harnessing the intrinsic rotating energy of the rotor system to realize self-powered rotor condition monitoring for the Internet of Things (IoT). It is promising to solve the issue of sustainable energy supply of the rotor monitoring system and achieve a self-powered IoT. In this work, we propose a novel maximal utilization strategy for piezoelectric-electromagnetic-triboelectric energy harvesting in a broad speed range and achieve the self-powered rotor condition monitoring system. The piezoelectric energy harvester (PEH), triboelectric nanogenerator (TENG), and electromagnetic energy harvester (EMH) are respectively arranged in the place where the system exhibits the maximum strain, the maximum contact area, and the maximum displacement, respectively, which can make full use of their characteristics in the structural space. The modulation boundaries (parts of TENG) render a more controllable dynamic behavior of the harvester, and realize the vibration and impact coordinated power generation mode, which can harvest more mechanical energy in the time domain. The theoretical mathematical model and working criteria of the proposed system are established and verified experimentally. In addition, the prototype can operate effectively in a wide speed range (0–1000 r/min) and it can charge a 100 μF capacitor to 5 V within 11 s. The self-powered rotor wireless temperature monitoring and self-powered wireless tire pressure monitoring are realized during the practical road tests. The maximum utilization strategy provides a new design methodology for hybrid energy harvesting, which has potential applications in intelligent driving and rotating machinery condition monitoring.

Sounds and acoustic emission-based early fault diagnosis of induction motor: A review study

Nowadays, condition-based maintenance (CBM) and fault diagnosis (FD) of rotating machinery (RM) has a vital role in the modern industrial world. However, the remaining useful life (RUL) of machinery is crucial for continuous monitoring and timely maintenance. Moreover, reduced maintenance costs, enhanced safety, efficiency, reliability, and availability are the main important industrial issues to maintain valuable and high-cost machinery. Undoubtedly, induction motor (IM) is considered to be a pivotal component in industrial machines. Recently, acoustic emission (AE) becomes a very accurate and efficient method for fault, leaks and fatigue detection and monitoring techniques. Moreover, CM and FD based on the AE of IM have been growing over recent years. The proposed research study aims to review condition monitoring (CM) and fault diagnosis (FD) studies based on sound and AE for four types of faults: bearings, rotor, stator, and compound. The study also points out the advantages and limitations of using sound and AE analysis in CM and FD. Existing public datasets for AE based analysis for CM and FD of IM are also mentioned. Finally, challenges facing AE based CM and FD for RM, especially for IM, and possible future works are addressed in this study.

Smart metasurface shaft for vibration source identification with a single sensor

In vibration monitoring of rotating machinery, traditional methods necessarily rely on multiple sensors to locate the vibration sources on the shaft. Reducing the number of sensors is an engineering challenge for on-shaft vibration source identification. This paper proposed a smart metasurface shaft (SMST) to achieve single-sensor identification of vibration sources. For the proposed SMST, a thin metasurface consisting of rubber metal rings is designed to cover the shaft and is capable of smartly on-shaft sensing of vibration sources. For the metasurface, the rubber metal rings with random metal masses are used to construct random local resonator units, which leads to the random modulation of location-different vibration responses. Theoretical derivation and simulations demonstrate the mechanism of random modulation of on-shaft vibrations. By combining the SMST with the compressive sensing algorithm, the vibration source locations can be reconstructed from the measurement of a single sensor. For the proposed SMST, experiments demonstrate that the single-sensor vibration source identification can be achieved with a high correct recognition ratio. The proposed SMST provides a new idea on single-sensor on-shaft vibration sensing, which shows potential applications in rotor dynamics, such as rotating machinery condition monitoring, vibration identification, and fault diagnosis.

A low quiescent power wireless rotating machinery condition monitoring system

Vibration-based monitoring of rotating machinery is rapidly evolving within the aerospace industry with priority on detecting impending failures. The workhorse of such monitoring system remains a piezoelectric-based accelerometers which requires a wired-harnesses, connectors, significant power, and signal conditioning, etc. Raytheon Technologies Research Center (RTRC) along with Collins Aerospace and Sandia National Laboratory have jointly developed an Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE). &#x0D; This is a low power alternate for a conventional wired vibration-based monitoring system. This self-contained sensor system includes: (1) a low quiescent power sensing element with a wake-up module, (2) a wireless communication module, and (3) a coin-cell battery. Leveraging work performed under Defense Advanced Research Projects Agency (DARPA) N-Zero program. This wireless health monitoring system can operate in a quiescent low power mode (~10nW) for a period of several years without servicing. With an exceedance above a preset vibration level (at designate characteristic frequencies), the sensor wakes up and wirelessly sends a warning of a precursor-to-failure.&#x0D; The ARISE sensor and wake-up module package has been validated with a replicated vibration environment acquired from a selected rotating machinery subject to progressive damage at the Structural Dynamics Laboratory at RTRC. The failure precursor is successfully detected by the sensor which triggers the wake-up module.&#x0D; This research was developed with funding from the Defense Advanced Research Projects Agency (DARPA) Micro Technology Office (MTO), under Aluminum-Nitride Resonant Integrated Accelerometer Sensors (ARISE) program.

Nonconvex Group Sparsity Signal Decomposition via Convex Optimization for Bearing Fault Diagnosis

Bearing fault diagnosis is critical for rotating machinery condition monitoring since it is a key component of rotating machines. One of the challenges for bearing fault diagnosis is to accurately realize fault feature extraction from original vibration signals. To tackle this problem, the novel group sparsity signal decomposition method is proposed in this article. For the sparsity within and across groups’ property of the bearing vibration signals, the nonconvex group separable penalty is introduced to construct the objective function, leading to that the noise between the adjacent impulses can be eliminated and the impulses can be effectively extracted. Furthermore, since the penalty function is nonconvex, the convexity condition of the corresponding objective function to the global minimum is discussed. In addition, to improve the efficiency of parameter selection, this article presents an adaptive regularization parameter selection strategy. Simulation and experimental studies show that compared with the traditional method, the proposed method can better preserve the target components and reducing uncorrelated interference components for bearing fault diagnosis.

Order spectrogram visualization for rolling bearing fault detection under speed variation conditions

Abstract For rotating machinery condition monitoring and fault diagnosis under speed variation conditions, order tracking has been considered as a very powerful non-stationary vibration signal analysis method, when compared with the frequency analysis methods based on stationary assumption. However, the conventional order tracking methods require additional hardware to provide a phase reference signal. This constraint limits the conventional methods in industrial applications significantly. In order to further improve the applicability of the conventional order tracking methods, some tacho-less order tracking methods have been proposed in the past few years. Despite the tacho-less order tracking methods successfully getting rid of reference signal, the algorithms are very complex and the computational burden is increased correspondingly. To address the aforementioned shortcomings, a straightforward tacho-less order tracking method based on order spectrogram visualization is proposed in this paper. In the proposed method, a ridge extraction approach is used to estimate the instantaneous frequency of a certain rotating frequency harmonic. And then the vibration signal is resonance demodulated and the time-frequency distribution of the demodulated signal is obtained. A subsequent transform is conducted and the frequency axis of the time-frequency distribution is rescaled based on the estimated instantaneous frequency of rotating shaft. Then, an order spectrogram is constructed and thereby the non-stationary interference introduced by rotating speed fluctuation is suppressed. Finally, fault orders are uncovered and bearing fault type can be identified. The effectiveness of the proposed method has been validated by both simulated and experimental rolling bearing vibration signals. The results illustrate the improved features regarding previously developed tacho-less order tracking method in bearing diagnosis under speed variation conditions.

Design and Realization of Rotating Machinery Conditions Monitoring System Based on LabVIEW

Abstract: Nonlinear dynamic analysis of rotating machinery system has always been the hot spot of the rotational dynamics research. This article sets up a rotating machinery condition monitoring system to realize the measurement of system dynamic characteristic parameters based on NI(National Instruments) virtual instruments technology. The measurement of vibration signal of rotating machinery system is achieved by using NI company general data acquisition module of NI Company. Meanwhile, by analyzing and processing the acquired data using LabVIEW 2012, the dynamic characteristics, such as .the speed of the rotating machinery system, the axis trajectory, spectrum parameters, are attained. The measurement results show that the rotating machinery condition monitoring system based on LabVIEW is easy to operate, easy to realize the function extension and maintenance, and that it can be used in the industrial engineering projects with rotation characteristics. LabVIEW as the development tools used by virtual instrument function is very powerful data acquisition software products support is one of the features of it, so using LabVIEW programming and data acquisition is simple and convenient.

Feature Extraction Method for Condition Monitoring of Rolling Element Bearings Based on Dual-Tree Complex Wavelet Packet Transform and VMD

The feature extraction of rolling element bearings vibration signals is one of the key issue for high-speed rotating machinery condition monitoring. A new scheme based on Dual-Tree Complex Wavelet Packet Transform (DTCWPT) and Variational Mode Decomposition (VMD) for extracting vibration condition monitoring feature is proposed. First, DTCWPT is used to reduce noise and pseudo frequency components from vibration signals by the energy ratio. Second, a set of Intrinsic Mode Function components (IMFs) can be got by VMD. Then, the energy ratio between the screening vibration signal and IMFs are calculated. And, the corresponding IMFs are selected according to the energy ratio threshold. Finally, applying the spectrum analysis technology, the condition monitoring feature can be extracted from the reconstructing signal. The experimental results of simulation signals and practical rolling element bearings vibration signals show that the scheme is feasible and effective for extracting the bearings operation state feature.

Mobile device-based shaft speed estimation

Abstract One of the fundamental parameters that needs to be fed into a rotating machinery condition monitoring system is the rotational speed of shaft operating in the system. This parameter is essential in order to derive other characteristic frequencies that may contain some valuable diagnostic information. There are many dedicated hardware elements used in determining the rotational speed of elements however the installation of them usually translates to additional cost of the entire condition monitoring scheme, especially if retrofitting is concerned, and may not always be financially viable due to potentially long payback period on the investment, as in the case of e.g. LV induction motors. This text investigates the possibility of assessing the rotational speed of a shaft based solely on the capabilities of a standard, off-the-shelf mobile phone. The results presented in this text seem to indicate that with an appropriate signal processing approach, it is possible to ascertain the speed of the rotating element using a tool available nowadays to virtually all service technicians.

A divide-and-compress lossless compression scheme for bearing vibration signals in wireless sensor networks

Remote rotating machinery condition monitoring system based on wireless sensor networks (WSNs) is an attractive application recently. Bearing is the critical important component in rotating machinery. The bearing vibration-based condition monitoring requires huge amount of vibration data. This is a big challenge to the limited resources of WSNs. So many data compression methods are proposed, but less focus on the lossless compression. In this paper, a novel lossless compression scheme based on divide-and-compress strategy is proposed, which combines the lossy compression into the lossless compression framework to enhance the compression capability. First, the discrete cosine transform is employed in data dividing to split original data into two parts that have different data characteristics. Then, according to the characteristics, several specially designed schemes are exploited for encoding the data. As described in the experimental results, the proposed compression scheme can effectively compress bearing vibration signals without data loss.

Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery

A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness). Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM) with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention. After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space. The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space. To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes. Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.