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This article suggests that the correlation information, hidden in spatial configuration and temporal dynamic of frequencies, is an important indication for bearing health condition. To consider this information, we extend graph-modeling strategy, and introduce a bearing fault detection and diagnosis technique based on temporal-spatio graph. First, short-time periodogram is extracted from vibration signal, and, then, modeled by a temporal-spatio graph. In fault detection phase, the spectrum of temporal channel graph is used to map short-time periodogram to acquire the so-called graph-mapped spectrum (GMS). The principal frequency in resulting GMS is found highly related with the health condition of monitored bearing. Thus, any change of health condition can be detected by checking this principal frequency over time. Once a fault is detected, the spatio channel graph is fed to K-nearest neighbor classifier, coupled with a specific graph distance metric, for fault type identification. Comprehensive experiments on two benchmarking datasets along with theoretical interpretation demonstrate the superiority of proposed method over state of the arts. The proposed temporal-spatio graph provides a significant extension of existing spectrum analysis for fault detection and diagnosis.

A smart factory is a highly digitized and connected production facility that relies on smart manufacturing. Additionally, artificial intelligence is the core technology of smart factories. The use of machine learning and deep learning algorithms has produced fruitful results in many fields like image processing, speech recognition, fault detection, object detection, or medical sciences. With the increment in the use of smart machinery, the faults in the machinery equipment are expected to increase. Machinery fault detection and diagnosis through various deep learning algorithms has increased day by day. Many types of research have been done and published using both open-source and closed-source datasets, implementing the deep learning algorithms. Out of many publicly available datasets, Case Western Reserve University (CWRU) bearing dataset has been widely used to detect and diagnose machinery bearing fault and is accepted as a standard reference for validating the models. This paper summarizes the recent works which use the CWRU bearing dataset in machinery fault detection and diagnosis employing deep learning algorithms. We have reviewed the published works and presented the working algorithm, result, and other necessary details in this paper. This paper, we believe, can be of good help for future researchers to start their work on machinery fault detection and diagnosis using the CWRU dataset.

Bearings are vital part of rotary machines. A failure of bearing has a negative impact on schedules, production operation and even human casualties. Therefore, in prior achieving fault detection and diagnosis (FDD) of bearing is ensuring the safety and reliable operation of rotating machinery systems. However, there are some challenges of the industrial FDD problems. First, there are unbalanced samples because industrial faults rarely occur. Conse-quently, the labeled data which can refer to failure information are limited in the industry and data augmentation methods are critical pre-processing be-fore training data driven models. Second, due to many learnable parameters in model and data of long sequence, both lead to time delay for FDD. There-fore, this paper proposes various data preprocessing methods and Light-Convolutional Neural Network (LCNN).

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10 -6 , and the method is most effective at lower shaft speeds. publishedVersion

Wheelset bearings are crucial mechanical components of high-speed trains. Wheelset-bearing fault detection is of great significance to ensure the safety of high-speed train service. Convolution sparse representations (CSRs) provide an excellent framework for extracting impulse responses induced by bearing faults. However, the performance of CSR on extracting impulse responses is fairly sensitive to inappropriate selection of method-related parameters, and a convolution model for representing the impulse responses has not been discussed. In view of these two unsolved problems, a convolutional representation model of the impulse response series is developed. A novel fault detection method, named adaptive CSR (ACSR), is then proposed based on combinations of CSR and methods for estimating three parameters related to CSR. Finally, the effectiveness of the proposed ACSR method is validated via simulation, bench testing, and a real-life running test employing a high-speed train.

A wheelset bearing is one of the crucial mechanical parts in a high-speed train. Its fault detection is of great significance, as it ensures the safety of a high-speed train service. To resolve the boundary changeability of the sidebands and false segments caused by measured noises and vibrational interferences, a novel automatic fault detection method of the wheelset bearing, named AFDMLEWT, is proposed based on the proposed multi-level empirical wavelet transform (MLEWT) and the developed segment selection method, named SSESKDB. MLEWT provides a complete framework for extracting the sidebands with different bandwidths distributed throughout different frequency positions. A novel index for evaluating the fault information capacity contained in different decomposition bandwidths, named ESKDB, is proposed. The segment selection method based on ESKDB, named SSESKDB, is proposed to select those segments of MLEWT containing the fruitful fault information in the full frequency range of wheelset bearing fault detection. Finally, the proposed method is then validated using two simulated signals and bench tests.

Vibration signal analysis is an important means for bearing fault detection/diagnosis and bearings often operate under time-varying rotational speed conditions. This data article contains vibration datasets collected from bearings with different health conditions under different time-varying speed conditions. The health conditions of the bearing include healthy, faulty with an inner race defect, and faulty with an outer race defect. The operating rotational speed conditions for the dataset include increasing speed, decreasing speed, increasing then decreasing speed, and decreasing then increasing speed. Mendeley Data, http://dx.doi.org/10.17632/v43hmbwxpm.1 .

With the rapid development of science and technology, the production facilities are also growing advanced. An intelligent production facility is the outcome of smart systems used within a factory. The smart factories yield more production; thus, the faults in the machinery are prompt to increase when they are operated on a daily basis and for almost all applications. Different deep learning-based methods have been used and implemented in detecting and diagnosing bearing faults using raw vibration data. To detect and analyze the machinery bearing faults, we have proposed a deep learning-based convolutional neural network, which uses the 2-D image representation of 1-D raw vibration data from the Case Western Reserve University (CWRU) bearing dataset as input. With the use of the data augmentation technique for increasing training data, the proposed model has achieved 99.38% accuracy. The proposed method is computationally less expensive and simple than most of the complex algorithms used for detecting and diagnosing the bearing faults.

Bearings are one of the most essential parts of rotary machines. The failure of bearings can lead to significant financial loss as well as personal casualties. Therefore, bearing defect diagnosis is a very important research project. Recently, a lot of bearing defect diagnosis studies using deep learning methods have been conducted. However, there are some challenges to be addressed. In a real working condition, there is always much more normal data than fault data, so a data imbalance problem exists. To address this situation, data augmentation method which generates more training data from the original data, was used. This method was done by applying a geometric transformation so that the class label did not be changed. Therefore, in this paper, we compared the results of using and without data augmentation technique through 1-D CNN and 2-D CNN deep learning algorithm that are effective on time series data analysis and pattern recognition. Finally, we obtained better results when using data augmentation technique.

International audience

Bearings are vital part of rotary machines. A failure of bearing has a negative impact on schedules, production operation and even human casualties. Therefore, in prior achieving fault detection and diagnosis (FDD) of bearing is ensuring the safety and reliable operation of rotating machinery systems. However, there are some challenges of the industrial FDD problems. Since according to a literature review, more than half of the broken machines are caused by bearing fault. Therefore, one of the important thing is time delay should be reduced for FDD. However, due to many learnable parameters in model and data of long sequence, both lead to time delay for FDD. Therefore, this paper proposes a deep Light Convolutional Neural Network (LCNN) using one dimensional convolution neural network for FDD.

The behaviour of vibrations is widely used for non-intrusive inspection and health monitoring of bearings. However, automated methods, intended for predicting the bearing status, greatly depend on ...

Timely and accurate bearing fault detection and diagnosis is important for reliable and safe operation of industrial systems. In this study, performance of a generic real-time induction bearing fault diagnosis system employing compact adaptive 1D Convolutional Neural Network (CNN) classifier is extensively studied. In the literature, although many studies have developed highly accurate algorithms for detecting bearing faults, their results have generally been limited to relatively small train/test data sets. As opposed to conventional intelligent fault diagnosis systems that usually encapsulate feature extraction, feature selection and classification as distinct blocks, the proposed system takes directly raw time-series sensor data as input and it can efficiently learn optimal features with the proper training. The main advantages of the 1D CNN based approach are 1) its compact architecture configuration (rather than the complex deep architectures) which performs only 1D convolutions making it suitable for real-time fault detection and monitoring, 2) its cost effective and practical real-time hardware implementation, 3) its ability to work without any pre-determined transformation (such as FFT or DWT), hand-crafted feature extraction and feature selection, and 4) its capability to provide efficient training of the classifier with limited size of training data set and limited number of BP iterations. Effectiveness and feasibility of the 1D CNN based fault diagnosis method is validated by applying it to two commonly used benchmark real vibration data sets and comparing the results with the other competing intelligent fault diagnosis methods. Scopus

The excellent features of bearing vibration signal are gainful to make accurate diagnosis results on the type of bearing damage. In this paper, a signal analysis technique based on wavelet packet t...

The characteristics of vibrations is one which is widely used for the non-intrusive inspection and health monitoring of bearings. However, automated methods, intended for predicting the health stat...

This paper proposes a fusion model to enhance classification accuracy of support vector machines (SVMs) for fault detection. The proposed method consists of two different phases, where in the first phase, different SVMs are constructed based on training datasets, and these trained SVMs are evaluated with respect to test datasets by calculating distances between test samples and trained hyperplanes. In order to achieve better results, an optimization scheme based on particle swarm optimization (PSO) is employed to adjust the SVMs parameters. In the next phase, a fusion model, in which the attained accuracies and distances are considered as inputs, is constructed. The fusion model utilizes zSlices-based representation of general type-2 fuzzy logic systems to combine different SVMs. The proposed approach is then applied for bearing fault detection of an induction motor with inner and outer race defects. To investigate the effectiveness of the proposed method, the general type-2 and type-1 fuzzy sets are compared with other two state-of-The-Art techniques. The obtained results confirm the superiority of the proposed approach.

In order to realize single fault detection (SFD) from the multi-fault coupling bearing data and further research on the multi-fault situation of bearings, this paper proposes a method based on features self-extraction of a Sparse Auto-Encoder (SAE) and results fusion of improved Dempster&ndash, Shafer evidence theory (D&ndash, S). Multi-fault signal compression features of bearings were extracted by SAE on multiple vibration sensors&rsquo, data. Data sets were constructed by the extracted compression features to train the Support Vector Machine (SVM) according to the rule of single fault detection (R-SFD) this paper proposed. Fault detection results were obtained by the improved D&ndash, S evidence theory, which was implemented via correcting the 0 factor in the Basic Probability Assignment (BPA) and modifying the evidence weight by Pearson Correlation Coefficient (PCC). Extensive evaluations of the proposed method on the experiment platform datasets showed that the proposed method could realize single fault detection from multi-fault bearings. Fault detection accuracy increases as the output feature dimension of SAE increases, when the feature dimension reached 200, the average detection accuracy of the three sensors for bearing inner, outer, and ball faults achieved 87.36%, 87.86% and 84.46%, respectively. The three types&rsquo, fault detection accuracy&mdash, reached to 99.12%, 99.33% and 98.46% by the improved Dempster&ndash, Shafer evidence theory (IDS) to fuse the sensors&rsquo, results&mdash, is respectively 0.38%, 2.06% and 0.76% higher than the traditional D&ndash, S evidence theory. That indicated the effectiveness of improving the D&ndash, S evidence theory by evidence weight calculation of PCC.

The highly overlapping distortion characteristic of high speed aero-engine bearing faults violates the fundamental assumption of popular bearing fault diagnostic techniques which assume that every impulse has a distinct exponential-decaying pattern. Therefore, a tailored clustering low rank framework (coined as CluLR) is proposed for the feature detection of aero-engine bearings. This work firstly explores the underlying prior information that fault features demonstrate multiple similarity structures in a transformed data matrix obtained through employing an elaborately designed partition operator. Then, incorporating the clustering procedure into low-rank regularization model, the proposed CluLR guarantees that different similarity information is reliably concentrated onto their matched low-rank domains, which effectively eliminates the singular value overlapping coherent pathology. Consequently, weak features as well as strong features could be detected simultaneously. Moreover, an alternative minimization algorithm adopted from block coordinate descent framework is developed to solve the two-stage nonsmooth and nonconvex problem. Lastly, compared with the state-of-the-art bearing diagnosis techniques, the proposed CluLR’s superiority is sufficiently verified through its application to the experimental data from an aero-engine bearing under 25000 rev/min for overlapping distorted feature detection tasks.

Although promising results of high-performance AI algorithms have been reported in recent predictive maintenance researches, most of the existing studies merely deal with AI-only solutions and do not consider the interaction between humans and AI. In this study, we explicitly focus on the benefits of interactions where a human inspector is assisted by AI solutions. A case study is conducted for predictive maintenance of wind farms, where endoscopic images were used for bearing fault detection. The experiment consisted of 54 technical inspectors and 2301 images collected over 138 wind turbines, and each inspector was shown images and asked to identify bearing faults in the absence and presence of AI-assistance. The results showed that AI-assistance had a statistically significant impact on improving the technical inspector’s specificity and time efficiency. The level of improvement was dependent on the level of expertise, where the generalist group showed greater improvements in specificity and time efficiency (24.6% and 25.3%, respectively) when compared with the specialist group (4.7% and 6.4%, respectively). Both groups responded positively on the reuse intention and usefulness of AI-assistance, and the change in cognitive load was not statistically significant.

In the present study, a new bearing fault detection and recognition methodology is proposed based on complementary ensemble empirical mode decomposition method (CEEMD) and a newly developed weighted multiscale entropy method. The need for this methodology is felt due to the inability of the existing multiscale entropy methods in correctly identifying the nature of the signal, particularly in the initial scales. The implication of this drawback is strongly perceived in the experimental analysis in the present work. Vibration signals acquired from test machines/working machines have a substantial presence of noise which severely affects the consistency and reliability of the extracted features. Therefore, for effective implementation and comparing the efficiency of the proposed methods, the original signal is firstly processed with CEEMD. The processing of the signal includes its decomposition into several modes thereafter reconstructing a new signal from the modes chosen through Hurst exponent threshold analysis. From the reconstructed signals, the faulty feature vectors are extracted by the weighted multiscale entropy methods. The capabilities of the proposed method are intensively tested through simulation and experimental analysis. From the analysis of simulated signals, it is demonstrated that the drawback prevailing in the established entropy methods have strongly been mitigated by the newly developed weighted entropy methods. On the experimental front, an impressive improvement is observed by the proposed methods both qualitatively (in indicating the faulty system from the normal system) and quantitatively (in recognizing the fault type and severity by the support vector machine classifier). Apart from the analysis of vibration signals, the versatility of the proposed method is also verified on the acoustic signals acquired under similar experimental conditions.

Robust identification of bearing health states is closely linked to timely condition monitoring and downtime reducing for rotating machinery. Although many proposed algorithms achieve extraordinary performances on feature extraction, uncertainty still remains for the bearing fault identification. To address this problem, this paper introduces a two-stage framework for bearing fault detection using graph similarity evaluation. The recognition stage is used to identify the operation state (fault or not) based on an improved graph-based method according to the sampled vibration signal for each spindle turn. The feature extraction stage, on the other hand, is implemented to extract the fault characters from the time-domain signals. The results indicate that the proposed method achieves 100% identification accuracy in bearing fault detection even with phase shifts. This work therefore provides a powerful tool for bearing faults detection and is broadly applicable to a variety of engineering applications and experimental conditions.

Rolling element bearings are machinery elements that are widely used in aerospace and industrial applications to support rotating shafts, thereby reducing mechanical friction and heating. However, these components are subject to several kinds of faults which, in general, can be divided into single-point or localized and distributed faults. A common way to detect ball bearing localized or raceway faults is by identifying and analyzing the so-called characteristic “bearing frequencies.” This article presents an approach to bearing condition-based maintenance which comprises fault detection and identification and condition prognosis, based on spectral kurtosis and squared envelope spectral analysis of stator current. In this work, envelope spectrum analysis, based on spectral kurtosis, is applied to identify these frequencies and to evaluate its applicability as a severity estimation index. To evaluate the performance of this index, experimental tests were carried out considering bearings with outer ...

This paper presents a bearing fault detection method for permanent magnet synchronous motors (PMSMs). Owing to its good characteristics, PMSM usage has increased; however, a substantial number of motor failures are also reported. Many studies have focused on bearing fault detection using vibration sensors. However, current sensors are already installed in many industries, and therefore, if bearing faults can be detected using these sensors, there would be no need to install additional sensors. A frequency analysis is performed to detect bearing faults and fast Fourier transform (FFT)-based methods can be used for the same. FFT needs to have a high resolution to be able to differentiate between the frequencies of bearing faults from those of the stator current. However, FFT requires extensive data and high computational cost to achieve this high resolution. Therefore, the zoom FFT (ZFFT) algorithm is implemented to minimize the computational cost and to increase the resolution. The experimental results verify the effectiveness of the proposed method by comparing FFT and ZFFT waveforms.

Rolling bearings are very commonly used in many industrial applications. Therefore, detecting problems in their performance is very essential. This can be done by analyzing vibration signals resulting from their operation, as recorded by accelerometers. The current investigation aims to evaluate the efficiency of various computational intelligence algorithms, in detecting and correctly classifying faults in rolling bearings. A supplementary goal is to determine the optimum location for the accelerometers, in order for the aforementioned algorithms to identify faults on each bearing. Results indicate the most efficient computational intelligence methods for fulfilling the aforementioned goals, and suggest an optimum experimental setup, in order to successfully detect bearing faults.

The support vector machine (SVM) does not have a fixed parameter selection method and the manual selection of parameters is difficult to determine the validity, which affects the accuracy of recognition. simultaneously, The existing coarse-grained approach cannot effectively analyze the high-frequency components of time series. In view of the shortcomings of the above method, we put forward a new technique of rolling bearings for fault detection, which combines wavelet packet dispersion entropy (WPDE) and artificial fish swarm algorithm (AFSA) optimize support vector machines (AFSA-SVM). First of all, wavelet packet is devoted to decompose the original vibration signal into components of different frequency bands. Secondly, the dispersion entropy (DE) are calculated for each of the obtained frequency band components to acquire more comprehensive and complete fault information. Afterward, Input feature samples into the SVM model for training, and AFSA is used to optimize the parameters of SVM to obtain the optimal value so as to establish the best classification model. Finally, the prepared test set is input into AFSA-SVM for fault classification. The achievement of bearing detection experiments show that this approach can accurately and quickly identify fault types.

Bearings are one of the most critical components in electric motors, gearboxes and wind turbines. Therefore, bearing fault detection and prognosis of remaining useful life are important to prevent productivity losses. In this study, a novel method is proposed for prognosis of bearing life using an autoencoder and recurrent neural networks-based prediction algorithm. Promising results have been obtained from the experimental data. A monotonic upward trend of the produced health indicator is obtained for all test cases, being one of critical indicators of a proper prognosis. The remaining useful life estimation is moderately accurate under a limited data.

During the bearing fault detection and diagnosis, fault feature extraction is a key step whether for the qualitative or the quantitative. This paper proposes a new redundant fault feature extraction technique based on tunable Q-factor wavelet transform (TQWT), which can separates complex non-stationary signals due to its oscillatory behavior rather than the frequency band. With implementing using different couples of Q-factor and redundancy, energies of multi-scale sub-band signals are collected to characterize the failure symptoms. Two cases of experimental bearing datasets were investigated to examine the effectiveness of proposed method, the results illustrated its robustness compared with the single-scale method in bearing fault classification and performance degradation assessment.

Many researches on detecting machinery faults in the early stage are being conducted for the purpose of preventing failure and reducing maintenance effort simultaneously. In this paper, a bearing fault detection method for a railway traction motor through leakage currents is proposed. The proposed detection method combines octave band analysis and machine learning. The abnormality simulation experiments with an inner race fault bearing are conducted and the effectiveness of the proposed method is verified. From the experiments, it is confirmed that the proposed method can detect failures of railway traction system well at specific conditions and leakage currents have potentials to be used for a bearing fault detection.

This paper presents an experimental study for detection of inter-turn and bearing faults in electrical machines using a sensor and logic circuitry based display mechanism. In a number of techniques proposed earlier, sensors based measurement of voltage, current, temperature, and speed were achieved and transferred to computer for protection decision. But, those suggested techniques were engraved with the limitations that they were machine dependent and less efficient in case of multiplicity of faults apart from their high cost and requirement of expert knowledge. The cost effective logic mechanism developed in this work gives the flexibility to monitor multiple faults at incipient stage and take protection decisions. The programmability of the developed system with respect to threshold values of the parameters; makes it independent of machines and machines' ratings. The literature is void of such a system satiating the twin objectives of condition monitoring - fault detection and protection, in the most simple and user friendly manner.

Condition monitoring and fault diagnosis via vibration signal analysis play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback that the characteristic frequency of relevant fault could not be determined effectively with traditional method. In this work an ensemble empirical mode decomposition (EEMD) was applied in order to alleviate the mode mixing problem occurring in empirical mode decomposition (EMD). Nevertheless, the simulation of the experiment shows that the spectral kurtosis(SK) is unable to fully identify multi-faults of rotor-bearing system, when different faults excite different resonance frequencies. To overcome the shortcoming, a new multi-faults detection method based on EEMD and spectral kurtosis (SK) is applied for detecting multi-faults of rotor imbalance and bearings faults. The proposed method based on spectral kurtosis (SK) and EEMD demonstrates very good results in extracting fault characteristic information of rotating machineries. Condition monitoring and fault diagnosis via vibration signal analysis play an important role to avoid serious accidents. Aiming at the complexity of multiple faults in a rotor-bearing system and drawback that the characteristic frequency of relevant fault could not be determined effectively with traditional method. In this work an ensemble empirical mode decomposition (EEMD) was applied in order to alleviate the mode mixing problem occurring in empirical mode decomposition (EMD). Nevertheless, the simulation of the experiment shows that the spectral kurtosis(SK) is unable to fully identify multi-faults of rotor-bearing system, when different faults excite different resonance frequencies. To overcome the shortcoming, a new multi-faults detection method based on EEMD and spectral kurtosis (SK) is applied for detecting multi-faults of rotor imbalance and bearings faults. The proposed method based on spectral kurtosis (SK) and EEMD demonstrates very good results in extracting fault characteristic information of rotating machineries.

In this paper, a precise and intelligent deconvolution process is developed in order to further extend its applications in discovering bearing faults and characteristics. A new index, harmonics-to-noise ratio (HNR), is introduced to extract the period. At the same time by combining HNR with the characteristic of kurtosis which is sensitive to the impulsivity of the fault signal, a novel deconvolution norm named HNR-guided deconvolution (HNRGD) is established to improve the performances of detecting the periodic impulses without requiring any prior knowledge. The effectiveness of the proposed method is validated by both simulation and experiment. The results demonstrate the superiority of HNRGD in the extraction and diagnosis of REBs faults compared with the original MED and MCKD.

Rolling bearings are widely used in rotating equipment. Detection of bearing faults is of great importance to guarantee safe operation of mechanical systems. Acoustic emission (AE), as one of the bearing monitoring technologies, is sensitive to weak signals and performs well in detecting incipient faults. Therefore, AE is widely used in monitoring the operating status of rolling bearing. This paper utilizes Empirical Wavelet Transform (EWT) to decompose AE signals into mono-components adaptively followed by calculation of the correlated kurtosis (CK) at certain time intervals of these components. By comparing these CK values, the resonant frequency of the rolling bearing can be determined. Then the fault characteristic frequencies are found by spectrum envelope. Both simulation signal and rolling bearing AE signals are used to verify the effectiveness of the proposed method. The results show that the new method performs well in identifying bearing fault frequency under strong background noise.

Condition-based monitoring (CBM) is widely used methodology for the fault diagnosis, which provides the analysis for the safe and proper operations of any device or element. Vibration analysis is most accurate and reliable technique of CBM to reveal the condition of device or element. In this technique, fault can be diagnosed by analysing the vibration data acquired from accelerometer. Convolutional Neural Network (CNN) has emerged as one of the most widely used methodology in application of pattern recognition and acoustic data analysis. In this paper, CNN is used as back-end classifier for bearing fault detection. Vibration data is collected for three different conditions of bearings i.e. normal condition, inner race fault and outer race fault. Statistical features are extracted from vibration data and used as input to CNN classifier. Convolution filters are learned by training CNN and are used to detect the unique features for each condition of bearing. The obtained accuracy shows that CNN is very reliable and effective technique for bearing fault diagnosis. It exhibits good performance compared to peer algorithms.

© 2017 The Authors. Gears are significant component in a multiplicity of industrial applications such as machine tool and gearboxes. An unforeseen failure of gear may result in significant economic losses. Therefore this research propose fault detection improvement throught series of vibration signal processing techuiques. These techniques have been tested experimentally using vibration data collected from the transmission system of a CS-29 'Category A' helicopter gearbox under different bearing damage severity of the second planetary stage. Results showed successful improvement of bearing fault detection.

Bearings are commonly used in rotary machinery, whereas up to half of machinery malfunctions could be related to bearing defects. Unfortunately reliable fault detection systems still remain a challenging task, especially when bearing defect-related features are nonstationary. A new normalized Hilbert-Huang transform (NHHT) technique is proposed in this paper for vibration-based bearing fault detection. The NHHT for bearing fault detection takes two processes: firstly the vibration signal is denoised to highlight defect-related impulses; and secondly representative features are extracted for bearing fault detection. Vibration signal denoising is carried out by the use of the maximum kurtosis deconvolution filter to reduce impedance effect of transmission path of the measured vibration signal. A novel strategy based on D’Agostino-Person normality is suggested to enhance the distinctive intrinsic mode functions for representative features extraction and formulation for bearing fault detection. The effectiveness of the proposed NHHT technique is verified by a series of experimental tests corresponding to different bearing health conditions, and its robustness in bearing fault detection is examined by the use of data sets from a different experimental setup.

The rolling element bearing is a significant component in rotating machinery. Suitable bearing fault detection and diagnosis (FDD) is vital to maintaining machine operations in a safe and healthy state. To address this issue, an extended observer-based FDD method is proposed, which uses a variable structure feedback linearization observer (FLO). The traditional feedback linearization observer is stable, however, this technique suffers from a lack of robustness. The proposed variable structure technique was used to improve the robustness of the fault estimation while reducing the uncertainties in the feedback linearization observer. The effectiveness of the proposed FLO procedure for the identification of outer, inner, and ball faults was tested using the Case Western University vibration dataset. The proposed model outperformed the variable structure observer (VSO), traditional feedback linearization observer (TFLO), and proportional-integral observer (PIO) by achieving average performance improvements of 5.5%, 8.5%, and 18.5%, respectively.

The impact of motors breakdown and failures on mobile robot motor bearing is an important concern for robot industries. For this reason, predictive motor lifetime and bearing fault classification techniques are being investigated extensively as a method of decreasing motor downtime and enhancing mobile robot reliability. With increasing attention on neural network technologies, many researchers have carried out lots of the relevant experiments and analyses, very plentiful and important conclusions are obtained. In this article, a classification method based on discrete wavelet transform (DWT) and long short-term memory network (LSTM) a proposed to find and classify fault type of mobile robot permanent magnet synchronous motor (PMSM). First, a set of mobile robot motor vibration signal were collected by the sensors. Second, the obtained vibration signal is decomposed into six frequency bands by the DWT. Haar function is selected as the mother function in the processing. The energy of every frequency band was calculated as a classification feature. Thirdly, four classification features with high classification rate are obtained. The feature vector is used as input of the neural network, and the fault type is identified by LSTM classifier with deviation unit. From the results of the experiments provided in the paper, the method can detect the fault type accurately and it is feasible and effective under different motor speed.

In Modern industrial, bearings are common in rotating machines. The processing data of bearings are nonlinear, and they usually are complicated distribution, which contains both Gaussian and non-Gaussian distributions. If using a single data distribution detection method, it will result in detecting performance degradation. To solve the possible monitoring difficulties of complicated distribution and nonlinear characteristics in industrial systems, this paper proposes a KPCA-KICA-HSSVM hybrid strategy. The proposed method uses KPCA, KICA and HSSVM to establish detection model, these models are work collaboratively in monitoring the real time process data to detect the possible faults. The proposed approach was tested and validated via a set of experimental data collected from a bearing test rig. Experimental results demonstrate the effectiveness of this approach.

This article presents a simple method for the detection and diagnosis of bearing faults, by fusing the information coming from two accelerometers. The method relies on three simple and intuitive features, extracted from the data coming from accelerometers placed at two different sites of the system under investigation. Our preliminary results indicate that by using simple statistical measures, such as the elements of the covariance matrix of the two sensors, faults at an early stage can be detected. In our the proposed scheme, the extracted features are fed to a k-nearest neighbour classifier for diagnosis purposes or to an ensemble of one-class detectors, if only the information from normal situation is available. As it is proved, based on experimental results, in both scenarios a remarkably high detection/diagnostic performance is achieved. Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE

International audience; This paper aims to develop a condition monitoring architecture for induction machines, with focus on bearing faults. The main objective of this paper is to identify fault signatures at an early stage by using high-resolution frequency estimation techniques. In particular, we present two subspace methods, which are Root-MUSIC and ESPRIT. Once the frequencies are determined, the amplitude estimation is obtained by using the Least Squares Estimator (LSE). Finally, the amplitude estimation is used to derive a fault severity criterion. The experimental results show that the proposed architecture has the ability to measure the faults severity.

This paper describes, a novel innovative design for generating vibration signals in a bearing, which is in static mode not in dynamic mode like other usual measurements reported in literatures, for various types of bearing faults analysis supports users with a technique based on monitoring vibration signals. This paper reports the computed power spectrum from vibration signal clearly identifies the fault signature with its raise in amplitude. Hence, in an impulse excitation technique vibration analysis with computed power spectrum provides an efficient monitoring of fault in axle bearings in a short timing. Programmable System on Chip (PSoC) creator design and Virtual Instrument program written in LabVIEW, a graphical language, provides efficient implementation of impulse excitation technique possible in static test method in a minimal time.

International audience; In this paper, an original method to compute an indicator for efficient detection of bearing defaults in high speed synchronous machines is presented. This indicator is based on the statistical analysis of the stator current spectrum. The principle of the method is to compare the current spectrum to a healthy reference spectrum. The reference spectrum is used to center and reduce the current spectrum and provides an indication on its difference from the reference spectrum. A statistical based indicator is then constructed as the sum of the contributions of the centered reduced spectrum for different interesting frequency bands. This indicator has been tested on 2 different test campaigns, for 4 different speeds and compared a vibratory indicator. Results show that the proposed indicator evolves the same way than the vibratory indicator and provides with an efficient detection of bearing fault with only very few false alarms.

In this paper, some indicators are developed for efficient detection of bearing defaults in high speed synchronous machines. These indicators are based on the analysis of stator current. As bearing defect signatures can be tracked through amplitude increase of some current harmonics, two specific indicators have been built based on energy considerations and on the Spectral Kurtosis analysis. These indicators are tested on a real industrial fan equipped with ceramic balls, in its environment. Several measurements for different operating points are tested to validate the approach and to its robustness during long time tests. From an experimental comparison between a healthy fan and another with damaged bearings, a frequency selection is performed to identify the frequency ranges where the energy is the most sensitive to the considered faults. This actuator is used in an air conditioning fan in aeronautic applications.

International audience; The aim of this paper is to present some indicators developed for efficient detection of bearing defaults in high speed synchronous machines using a stator current analysis. These actuators are used in an air conditioning fan for aeronautic applications. The signatures of the bearing defects appear through an increase in amplitude of specific current harmonics multiples of the rotation frequency. From an experimental comparison between a healthy fan and another with damaged bearings, an automatic frequency selection is performed to identify the frequency ranges for which the energy is the most sensitive to the considered faults. From these frequencies, several strategies are investigated to propose a suitable indicator for the bearing fault detection. A post-processing algorithm is then developed and tested for different measurements, different types of faults and different operating points, to ensure the robustness of the proposed method.