125 research outputs found

    Rotate vector (Rv) reducer fault detection and diagnosis system: towards component level prognostics and health management (phm).

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    In prognostics and health management (PHM), the majority of fault detection and diagnosis is performed by adopting segregated methodology, where electrical faults are detected using motor current signature analysis (MCSA), while mechanical faults are detected using vibration, acoustic emission, or ferrography analysis. This leads to more complicated methods for overall fault detection and diagnosis. Additionally, the involvement of several types of data makes system management difficult, thus increasing computational cost in real-time. Aiming to resolve that, this work proposes the use of the embedded electrical current signals of the control unit (MCSA) as an approach to detect and diagnose mechanical faults. The proposed fault detection and diagnosis method use the discrete wavelet transform (DWT) to analyze the electric motor current signals in the time-frequency domain. The technique decomposes current signals into wavelets, and extracts distinguishing features to perform machine learning (ML) based classification. To achieve an acceptable level of classification accuracy for ML-based classifiers, this work extends to presenting a methodology to extract, select, and infuse several types of features from the decomposed wavelets of the original current signals, based on wavelet characteristics and statistical analysis. The mechanical faults under study are related to the rotate vector (RV) reducer mechanically coupled to electric motors of the industrial robot Hyundai Robot YS080 developed by Hyundai Robotics Co. The proposed approach was implemented in real-time and showed satisfying results in fault detection and diagnosis for the RV reducer, with a classification accuracy of 96.7%

    Holistic Fault Detection and Diagnosis System in Imbalanced, Scarce, Multi-Domain (ISMD) Data Setting for Component-Level Prognostics and Health Management (PHM)

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    In the current Industry 4.0 revolution, prognostics and health management (PHM) is an emerging field of research. The difficulty of obtaining data from electromechanical systems in an industrial setting increases proportionally with the scale and accessibility of the automated industry, resulting in a less interpolated PHM system. To put it another way, the development of an accurate PHM system for each industrial system necessitates a unique dataset acquired under specified conditions. In most circumstances, obtaining this one-of-a-kind dataset is difficult, and the resulting dataset has a significant imbalance, a lack of certain useful information, and contains multi-domain knowledge. To address those issues, this paper provides a fault detection and diagnosis system that evaluates and preprocesses imbalanced, scarce, multi-domain (ISMD) data acquired from an industrial robot, utilizing signal processing (SP) techniques and deep learning-based (DL) domain knowledge transfer. The domain knowledge transfer is used to produce a synthetic dataset with a high interpolation rate that contains all the useful information about each domain. For domain knowledge transfer and data generation, continuous wavelet transform (CWT) with a generative adversarial network (GAN) was used, as well as a convolutional neural network (CNN), to test the suggested methodology using transfer learning and categorize several faults. The proposed methodology was tested on a real experimental bench that included an industrial robot created by Hyundai Robotics. This test had a satisfactory outcome with a 99.7% (highest) classification accuracy achieved by transfer learning on several CNN benchmark models

    Deep Learning Aided Data-Driven Fault Diagnosis of Rotatory Machine: A Comprehensive Review

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    This paper presents a comprehensive review of the developments made in rotating bearing fault diagnosis, a crucial component of a rotatory machine, during the past decade. A data-driven fault diagnosis framework consists of data acquisition, feature extraction/feature learning, and decision making based on shallow/deep learning algorithms. In this review paper, various signal processing techniques, classical machine learning approaches, and deep learning algorithms used for bearing fault diagnosis have been discussed. Moreover, highlights of the available public datasets that have been widely used in bearing fault diagnosis experiments, such as Case Western Reserve University (CWRU), Paderborn University Bearing, PRONOSTIA, and Intelligent Maintenance Systems (IMS), are discussed in this paper. A comparison of machine learning techniques, such as support vector machines, k-nearest neighbors, artificial neural networks, etc., deep learning algorithms such as a deep convolutional network (CNN), auto-encoder-based deep neural network (AE-DNN), deep belief network (DBN), deep recurrent neural network (RNN), and other deep learning methods that have been utilized for the diagnosis of rotary machines bearing fault, is presented

    Condition Monitoring of Helical Gear Transmissions Based on Vibration Modelling and Signal Processing

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    Condition monitoring (CM) of gear transmission has attracted extensive research in recent years. In particular, the detection and diagnosis of its faults in their early stages to minimise cost by maximising time available for planned maintenance and giving greater opportunity for avoiding a system breakdown. However, the diagnostic results obtained from monitored signals are often unsatisfactory because mainstream technologies using vibration response do not sufficiently account for the effect of friction and lubrication. To develop a more advanced and accurate diagnosis, this research has focused on investigating the nonlinearities of vibration generation and transmission with the viscoelastic properties of lubrication, to provide an in-depth understanding of vibration generating mechanisms and hence develop more effective signal processing methods for early detection and accurate diagnosis of gear incipient faults. A comprehensive dynamic model has been developed to study the dynamic responses of a multistage helical gear transmission system. It includes not only time-varying stiffness but also tooth friction forces based on an elastohydrodynamic lubrication (EHL) model. In addition, the progression of a light wear process is modelled by reducing stiffness function profile, in which the 2nd and 3rd harmonics of the meshing frequency (and their sidebands) show significant alteration that support fault diagnostic at early stages. Numerical and experimental results show that the friction and progressive wear induced vibration excitations will change slightly the amplitudes of the spectral peaks at both the mesh frequency and its sideband components at different orders, which provides theoretical supports for extracting reliable diagnostic signatures. As such changes in vibrations are extremely small and submerged in noise, it is clear that effective techniques for enhancing the signal-to-noise ratio, such as time synchronous averaging (TSA) and modulation signal bispectrum (MSB) are required to reveal such changes. MSB is preferred as it allows small amplitude sidebands to be accurately characterised in a nonlinear way without information loss and does not impose any addition demands regarding angular displacement measurement as does TSA. With the successful diagnosis of slight wear in helical gears, the research progressed to validate the capability of MSB based methods to diagnose four common gear faults relating to gear tribological conditions; lubrication shortfall, changes in lubrication viscosity, water in oil, and increased bearing clearances. The results show that MSB signatures allows accurate differentiation between these small changes, confirming the model and signal processing proposed in this thesi

    Sensors Fault Diagnosis Trends and Applications

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    Fault diagnosis has always been a concern for industry. In general, diagnosis in complex systems requires the acquisition of information from sensors and the processing and extracting of required features for the classification or identification of faults. Therefore, fault diagnosis of sensors is clearly important as faulty information from a sensor may lead to misleading conclusions about the whole system. As engineering systems grow in size and complexity, it becomes more and more important to diagnose faulty behavior before it can lead to total failure. In the light of above issues, this book is dedicated to trends and applications in modern-sensor fault diagnosis

    Advanced signal processing methods for condition monitoring

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    Condition monitoring of induction motors (IM) among with the predictive maintenance concept are currently among the most promising research topics of manufacturing industry. Production efficiency is an important parameter of every manufacturing plant since it directly influences the final price of products. This research article presents a comprehensive overview of conditional monitoring techniques, along with classification techniques and advanced signal processing techniques. Compared methods are either based on measurement of electrical quantities or nonelectrical quantities that are processed by advanced signal processing techniques. This article briefly compares individual techniques and summarize results achieved by different research teams. Our own testbed is briefly introduced in the discussion section along with plans for future dataset creation. According to the comparison, Wavelet Transform (WT) along with Empirical Mode Decomposition (EMD), Principal Component Analysis (PCA) and Park's Vector Approach (PVA) provides the most interesting results for real deployment and could be used for future experiments.Web of Scienc

    Condition Monitoring and Fault Diagnosis of a Multi-Stage Gear Transmission Using Vibro-acoustic Signals

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    Gearbox condition monitoring(CM) plays a vital role in ensuring the reliability and operational efficiency of a wide range of industrial facilities such as wind turbines and helicopters. Many technologies have been investigated intensively for more accurate CM of rotating machines with using vibro-acoustic signature analysis. However, a comparison of CM performances between surface vibrations and airborne acoustics has not been carried out with the use of emerging signal processing techniques. This research has focused on a symmetric evaluation of CM performances using vibrations obtained from the surface of a multi stage gearbox housing and the airborne sound obtained remotely but close to the gearbox, in conjunction with state of the art signal processing techniques, in order to provide efficient and effective CM for gear transmissions subject to gradual and progressive deteriorations. By completing the comparative studies, this research has resulted in a number of new findings that show significant contributions to knowledge which are detailed as follows. In general, through a comprehensive review of the advancement in the subject, the research has been carried out by integrating an improved dynamic modelling, more realistic experiment verification and more advanced signal processing approaches. The improved modelling has led to an in-depth understanding of the nonlinear modulation in vibro-acoustic signals due to wear effects. Thereafter, Time Synchronous Average (TSA) and Modulation Signal Bispectrum (MSB) are identified to be the most promising signal processing methods to fulfil the evaluation because of their unique properties of simultaneous noise reduction and modulation enhancement. The more realistic tests have demonstrated that arun-to-failure test is necessary to develop effective diagnostic tools as it produces datasets from gear transmissions where deterioration naturally progresses over a long operation, rather than faults created artificially to gear systems, as is common in the majority of studies and the results unreliable. Particularly, the evaluation studies have clarified a number of key issues in the realisation of gearbox diagnostics based on TSA and MSB analysis of the vibrations from two accelerometers and acoustics from two microphones in monitoring the run-to-failure process, which showed slight gear wear of two back-to-back multiple stage helical gearboxes under variable load and speed operations. TSA analysis of vibration signals and acoustic signals allows for accurate monitoring and diagnosis results of the gradual deterioration in the lower speed transmission of both the tested gearboxes. However, it cannot give the correct indication of the higher speed stages in the second gearbox as the reference angle signal is too erroneous due to the distortion of long transmission trains. In addition, acoustic signals can indicate that there is a small determination in the higher speed transmission of the first gearbox. The MSB analysis of vibration signals and sound signals allows for the gathering of more corrective monitoring and diagnostic results of the deterioration in the four stages of transmissions of the two tested gearboxes. MSB magnitudes of both the two lower speed transmissions show monotonic increases with operational time and the increments over a longer period are in excess of three times higher than the baselines, the deteriorations are therefore regarded as severe. For the two higher speed transmissions, the MSB of vibrations and acoustics illustrates small deteriorations in the latter operating hours. Comparatively, acoustic signal based diagnostics can out-perform vibration as it can provide an early indication of deteriorations and correct diagnosis of the faults as microphones perceive a large area of dynamic responses from gearbox housing whereas accelerometers collect a very localised response which can be distorted by transmission paths. In addition, MSB analysis can out-perform conventional TSA as it maintains all diagnostic information regarding the rotating systems and can be implemented without any additional reference channels
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