Multiple Fault Diagnosis Research Articles

Fault Diagnosis for Power Converter in SRM Drives Based on Current Prediction

This article proposes an online diagnosis scheme for transistor faults of asymmetric half-bridge converter (AHBC) in switched reluctance motor (SRM) drives. In order to extract more information from the phase current to identify faults, based on the conventional current measurement method, two novel current measurement methods are presented by reconstructing current sensors. According to the novel current measurement methods, a current prediction method is raised. The phase current at the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> th sampling time is available so that the estimated current can be calculated. The fault can be detected and located by analyzing the given and actual switching states, which are deduced by comparing the measured and estimated currents. Compared with existing methods, the proposed scheme can be applied to diagnosis of multiple faults in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> -phase AHBC, which can operate at various control strategies and chopping modes. With a single current sensor in each phase, the scheme will not increase the cost and complexity of the SRM drive. Furthermore, the scheme does not require complicated computation, making it easy to implement online. The experimental results confirm the effectiveness and flexibility of the proposed scheme.

A composite learning approach for multiple fault diagnosis in gears

A major part of Prognostic and Health Management of rotating machines is dedicated to diagnosis operations. This makes early and accurate diagnosis of single and multiple faults an economically important requirement of many industries. With the well-known challenges of multiple faults, this paper proposes a new Blended Ensemble Convolutional Neural Network – Support Vector Machine (BECNN-SVM) model for multiple and single faults diagnosis of gears. The proposed approach is obtained by preprocessing the acquired signals using complementary signal processing techniques. This form inputs to 2D Convolutional Neural Network base learners which are fused through a blended ensemble model for fault detection in gears. Discriminative properties of the complementary features ensure the high capabilities of the approach to give good results under different load, speed, and fault conditions of the gear system. The experimental results show that the proposed method can accurately detect rotating machine faults. The proposed approach compared with other state-of-the-art methods indicates improved overall effectiveness for gear faults diagnosis.

Application of Improved Robust Local Mean Decomposition and Multiple Disturbance Multi-Verse Optimizer-Based MCKD in the Diagnosis of Multiple Rolling Element Bearing Faults

Rolling element bearings are an important joint in mechanical equipment and have a high engineering application value. To solve the problem of the difficulty in extracting periodic fault pulses due to complex noise interference and the interference of transmission paths in rolling element bearing fault characteristic signals, a novel hybrid fault diagnosis method based on complementary complete ensemble robust local mean decomposition with adaptive noise (CCERLMDAN) combined with multiple disturbance multi-verse optimizer (MDMVO)-based Maximum correlated Kurtosis deconvolution (MCKD) is proposed in this paper, and applied in different rolling element bearing fault conditions. Firstly, the CCERLMDAN method adaptively decomposes the fault vibration signal into multiple product functions (PF), and then selects the PF with the most fault information through the sensitive index (SI). Finally, the MDMVO method adaptively selects the best parameter combination of the MCKD method and then uses MCKD to perform a deconvolution operation on the selected PF, highlighting the periodic fault pulse excited by the bearing fault. The field-measured vibration signals of rolling element bearing faults are applied to verify the proposed method. The final results show that the method effectively improves the fault diagnosis accuracy of rolling element bearings, and both CCERLMDAN and MDMVO methods achieve a better performance than the original method.

A Voltage-Based Multiple Fault Diagnosis Approach for Cascaded H-Bridge Multilevel Converters

This article proposes a fast and robust open-circuit fault diagnosis method without additional sensors for cascaded H-bridge multilevel converters (CHBMCs). The method is based on the error between the actual input-side voltage and the estimated one, with one detection threshold and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2n$ </tex-math></inline-formula> comparators in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> cascaded H-bridges. Benefitted from the simple design of the fault location, spike elimination, and parameter estimation, with the lower implementation burden, the single and multiple faults in arbitrary positions are accurately identified within a fundamental cycle, and the diagnostic robustness is guaranteed under different transient changes. In view of the low complexity, the proposed scheme can be easily applied to the CHBMCs with arbitrary modules. The experimental results from a CHBMC prototype verify the effectiveness of the proposed method under different operating conditions and fault scenarios.

A self-adaptive multiple-fault diagnosis system for rolling element bearings

The inevitable simultaneous formation of multiple-faults in bearings generates severe vibrations, causing premature component failure and unnecessary downtime. For accurate diagnosis of multiple-faults, machine learning (ML) models need to be trained with the signature of different multiple-faults, which increases the data acquisition time and expense. This paper proposes a self-adaptive vibration signature-based fault diagnostic method for detecting multiple bearing faults using various single-fault vibration signatures. A time-frequency-based hybrid signal processing technique, which involves discrete wavelet transform and Hilbert transform, was adopted for signal decomposition, followed by the implementation of a sliding window-based feature extraction process. Seven optimized metaheuristic algorithms were used to find the best feature sets, which were further used for the training of three ML models. The results show that the proposed methodology has tremendous potential to detect multiple bearing fault conditions in any possible combination using single-fault data. This will be helpful where accessibility to large amounts of data is limited for multiple-fault diagnosis.

Vibration and infrared thermography based multiple fault diagnosis of bearing using deep learning

ABSTRACT The occurrence of multiple faults is a practical problem in the bearings of rotating machines, and early diagnosis of such issues in an intelligent manner is vital in the era of industry 4.0. The present work investigated various combinations of bearing faults, including dual and multiple fault conditions. Two prevalent fault diagnosis methods were employed: vibration monitoring using time-frequency scalograms extracted through Continuous Wavelet Transform (CWT) and a non-invasive Infrared Thermography (IRT). A 2-D Convolutional Neural Network (CNN) was used to classify various combinations of fault conditions through automated feature extraction. The proposed methodology was validated at two constant speed conditions of 19 Hz and 29 Hz and continuously accelerated and decelerated speed conditions in the range of 19 Hz - 29 Hz. Adequate accuracy was achieved in both dual and multiple fault conditions in the case of vibration-based fault diagnosis, with a range of 99.39 % to 99.97 %. Meanwhile, in the case of proposed IRT-based fault diagnosis, 100 % classification accuracy was achieved for dual and multiple faults in all conditions. These results signify the robustness and reliability of the proposed methodology for dual and multiple fault diagnosis in bearings at constant and varying speed conditions.

Multiple Industrial Induction Motors Fault Diagnosis Model within Powerline System Based on Wireless Sensor Network

The voltage supply of induction motors of various sizes is typically provided by a shared power bus in an industrial production powerline network. A single motor’s dynamic behavior produces a signal that travels along the powerline. Powerline networks are efficient at transmitting and receiving signals. This could be an indication that there is a problem with the motor down immediately from its location. It is possible for the consolidated network signal to become confusing. A mathematical model is used to measure and determine the possible known routing of various signals in an electricity network based on attenuation and estimate the relationship between sensor signals and known fault patterns. A laboratory WSN based induction motors testbed setup was developed using Xbee devices and microcontroller along with the variety of different-sized motors to verify the progression of faulty signals and identify the type of fault. These motors were connected in parallel to the main powerline through this architecture, which provided an excellent concept for an industrial multi-motor network modeling lab setup. A method for the extraction of Xbee node-level features has been developed, and it can be applied to a variety of datasets. The accuracy of the real-time data capture is demonstrated to be very close data analyses between simulation and testbed measurements. Experimental results show a comparison between manual data gathering and capturing Xbee sensor nodes to validate the methodology’s applicability and accuracy in locating the faulty motor within the power network.

A One-Class Generative Adversarial Detection Framework for Multifunctional Fault Diagnoses

In this article, fault diagnosis is of great significance for system health maintenance. For real applications, diagnosis accuracy suffers from unbalanced data patterns, where normal data are usually abundant than anomaly ones, leading to tremendous diagnosis obstacles. Therefore, it is challenging to use only normal data for fault diagnosis under this imbalanced condition. In addition, a single fault diagnosis model can only conduct one fault diagnosis task in most of cases. Accordingly, a one-class generative adversarial detection (OCGAD) framework based on semisupervised learning is proposed to learn one-class latent knowledge for dealing with multiple semisupervised fault diagnosis tasks, i.e., fault detection using only normal knowledge learning, novelty detection from unknown conditional data, and fault classification with unlabeled data. A bi-directional generative adversarial network (Bi-GAN) is first trained with only normal data. A one-class support vector machine is then established using features exacted by Bi-GAN from signals acquired from an attitude sensor for multifunctional fault detection. The presented OCGAD model is validated using an industrial robot with experiments of three fault detection tasks. The results demonstrate that the present model has good performance for dealing with multiple semisupervised diagnosis problems.

Multiple Open-Circuit Faults Diagnosis in Six-Phase Induction Motor Drives Using Stator Current Analysis

In recent years, multiphase machines have been presented as a good alternative to the classical three-phase machines due to their ability to operate under fault-tolerant conditions. However, the transition from pre- to post-fault control needs a reliable fault diagnostics algorithm. Accordingly, this article presents a reliable diagnostics approach that allows the detection and identification of multiple open-switches and open-phase faults in power converters fed six-phase induction motors, including adjacent and nonadjacent phases. The proposed method provides normalized numerical signatures from the measured output currents without requiring either tuning effort or detection thresholds. The reliability and robustness of the proposed algorithm are proven through experimental tests.

MFCC based ensemble learning method for multiple fault diagnosis of roller bearing

MFCC based ensemble learning method for multiple fault diagnosis of roller bearing

Multiple open-circuit fault diagnosis of three-phase rectifier based on current data reconstruction

ABSTRACT A novel and real-time fault diagnosis method based on current data reconstruction for rectifiers is proposed in this article. The symmetry of a 3-phase pulse-width modulation rectifier’s topology can be used for fault diagnosis. The symmetry is broken when open-circuit faults happen, a similarity measurement algorithm based on current data reconstruction is used for fault detection. Fault characteristics for current lists and multi-scale entropy are proposed to locate different kinds of faults. The proposed diagnostic method doesn’t need additional hardware devices and costs low. It is convenient to be embedded in an existing control system as a subprogram without significant adjustments. The experimental results show the effectiveness of the proposed diagnostic method for both single and multiple open-circuit faults.

Component level signal segmentation method for multi-component fault detection in a wind turbine gearbox

Condition monitoring of a modern wind turbine gearbox is quite challenging as it comes with multiple stages which operate at different frequencies. A gearbox is made up of multiple components and fault diagnosis (single or multi-component) could be challenging owing to the interaction between the mating parts and the damaged component. In this investigation, a simplified signal segmentation technique that segments the non-stationary vibration signals to match a specific speed stage and component within a multi-stage gearbox is proposed. This technique improves the features within the dataset and allow even simpler algorithms to be more effective while performing fault diagnosis. The segmentation approach is also evaluated for its robustness with three different machine-learning algorithms, namely Decision tree, Support Vector Machine and Deep Neural Network. The overall classification accuracy of the datasets prepared with the proposed approach is found to be 97%, which is higher when compared to the conventional approach.

Sensor Fault-Tolerant Control of Microgrid Using Robust Sliding-Mode Observer.

This work investigates sensor fault diagnostics and fault-tolerant control for a voltage source converter based microgrid (model) using a sliding-mode observer. It aims to provide a diagnosis of multiple faults (i.e., magnitude, phase, and harmonics) occurring simultaneously or individually in current/potential transformers. A modified algorithm based on convex optimization is used to determine the gains of the sliding-mode observer, which utilizes the feasibility optimization or trace minimization of a Ricatti equation-based modification of H-Infinity () constrained linear matrix inequalities. The fault and disturbance estimation method is modified and improved with some corrections in previous works. The stability and finite-time reachability of the observers are also presented for the considered faulty and perturbed microgrid system. A proportional-integral (PI) based control is utilized for the conventional regulations required for frequency and voltage sags occurring in a microgrid. However, the same control block features fault-tolerant control (FTC) functionality. It is attained by incorporating a sliding-mode observer to reconstruct the faults of sensors (transformers), which are fed to the control block after correction. Simulation-based analysis is performed by presenting the results of state/output estimation, state/output estimation errors, fault reconstruction, estimated disturbances, and fault-tolerant control performance. Simulations are performed for sinusoidal, constant, linearly increasing, intermittent, sawtooth, and random sort of often occurring sensor faults. However, this paper includes results for the sinusoidal nature voltage/current sensor (transformer) fault and a linearly increasing type of fault, whereas the remaining results are part of the supplementary data file. The comparison analysis is performed in terms of observer gains being estimated by previously used techniques as compared to the proposed modified approach. It also includes the comparison of the voltage-frequency control implemented with and without the incorporation of the used observer based fault estimation and corrections, in the control block. The faults here are considered for voltage/current sensor transformers, but the approach works for a wide range of sensors.

Detection and Diagnosis of Dependent Faults That Trigger False Symptoms of Heating and Mechanical Ventilation Systems Using Combined Machine Learning and Rule-Based Techniques

Detection and diagnosis of the malfunction of the heating, ventilation, and air conditioning (HVAC) systems result in more energy efficient systems with a higher level of indoor comfort. The information from the system combined with the artificial intelligence methods contributes to powerful fault detection and diagnosis. The paper presents a novel method for the detection and diagnosis of multiple dependent faults in an air handling unit (AHU) of HVAC system of an institutional building during heating season. The proposed method guided the search for faults, by using the information and operation flow between sensors. Support vector regression (SVR) models, developed from building automation system (BAS) trend data, predicted air temperature of two target sensors, under normal operation conditions without known problems. The fault symptom was detected when the residual of measured and predicted values exceeded the threshold. The recurrent neural network (RNN) models predicted the normal operation values of regressor sensors, which were compared with measurements, as the first step for the identification of fault symptoms. Rule-based models were used for fault diagnosis of sensors or equipment. Results from a case study of an existing building showed the quality of proposed method for the detection and diagnosis of the multiple dependent faults.

Diagnosis for multiple faults of chiller using ELM-KNN model enhanced by multi-label learning and specific feature combinations

Existing fault detection and diagnosis methods for chillers are usually very effective on single faults diagnosis, but perform poorly while diagnosing multiple faults, and these fault diagnosis models depend on the range of training data. Such models tend to fail when the system encounters a fault that was not included in training data. This paper presents a novel multiple faults diagnosis method for chiller based on multi-label learning and specific feature combinations enhanced ELM-KNN. Firstly, Extreme Learning Machine (ELM) is used for mapping of features to improve the accuracy of K-Nearest Neighbor (KNN) algorithm for multiple faults. Then, through multi-label learning, a model is established for each single fault, and by merging these models a final multiple faults diagnosis model is formed. Finally, by examining the input features, the specific combinations of input features for each single fault are determined and applied to the actual chiller. Experimental results show that the model proposed in this paper has the highest accuracy among the compared methods. The model proposed has achieved better diagnosis performance for the multiple faults in the absence of several multiple faults training data. The specific feature combinations are beneficial to improve the diagnosis performance of the model. The model can have excellent diagnostic performance for both normal operation and multiple faults by only using a small amount of single fault data in training. In addition, the model proposed can also complete the diagnosis of single faults in the absence of some single faults training data.

Aircraft robust data-driven multiple sensor fault diagnosis based on optimality criteria

A general robust data-driven scheme for the Fault Detection, Isolation and Estimation of multiple sensor faults is proposed and validated using multi-flight data records. Robustness to modelling uncertainty and noise is achieved through an optimized data-driven design of the three blocks that constitute the scheme. First, a robust Fault Detection (FD) filter given by the linear combination of previously identified Analytical Redundancy Relationships (AARs) is derived as the solution of a multi-objective optimization where the minimum fault sensitivity is maximized while the standard deviation (STD) of the filtered error, in nominal condition, is minimized. Then, a Fault Pre-Isolation (FPI) block is introduced to select a restricted number of sensors containing with high likelihood the subset of the faulty sensors. In this phase, robustness is achieved through the data-driven design of a redundant number of Multiple-ARRs and a voting logic. Finally, the robust Fault Isolation (FI) is achieved relying on the design of a large collection of additional AARs whose fault signatures are specifically designed to optimize, at the same time, noise immunity while maximizing the decoupling of the (pre-isolated) fault directions. A procedure based on fault amplitude reconstruction is proposed to isolate the set of faulty sensors sequentially. The proposed scheme has been applied to the design of a multiple Fault Diagnosis scheme for a set of 8 sensors of a semi-autonomous aircraft basing on multi-flight data. Validation results are compared with state-of-the-art multiple Fault Diagnosis schemes.

Simultaneous fault diagnosis based on multiple kernel support vector machine in nonlinear dynamic distillation column

AbstractAlthough numerous works have been done, most of the studies in fault diagnosis are limited to single fault type at a time. Majority of the works reported in the literature do not extend the diagnosis of the root cause of the fault for simultaneous faults specifically in the distillation column. However, an industrial system is susceptible to more than one fault at a time, which may or may not be interrelated. These faults not only reduce the diagnosis performance but also increase the computational complexity of the diagnosis algorithm. In this work, therefore, a multiple kernel support vector machine (MK‐SVM) algorithm is proposed to diagnose simultaneous faults in the distillation column. In the developed MK‐SVM algorithm, multilabel approach based on various kernel functions has been utilized for the classification of simultaneous faults. Dynamic simulation of a pilot‐scale distillation column using Aspen Plus® is used for generating data in normal and faulty operation. Eight different fault types are considered, including valve sticking at reflux and reboiler, tray upsets, loss of feed flow, feed composition, and feed temperature changes. In the classification of simultaneous faults, a combination of two, three, and four faults is introduced for the performance evaluation of the proposed MK‐SVM algorithm. The result showed that the proposed MK‐SVM has a high fault detection rate (FDR) of 99.51% and a very low misclassification rate (MR) of 0.49%. The MK‐SVM–based classification is better with the F1 score of &gt;97% for all combinations of faults. Moreover, it is observed that the proposed MK‐SVM shows better fault diagnosis for single, multiple, and simultaneous faults as compared to other established machine‐learning algorithms.

Gearbox Multiple Faults Diagnosis under Stationary and Non-Stationary Operating Conditions Using Convolutional Neural Networks

Gearbox Multiple Faults Diagnosis under Stationary and Non-Stationary Operating Conditions Using Convolutional Neural Networks

A currentless multiple switch open-circuit faults diagnosis strategy for modular multilevel converter with nearest level modulation in HVDC system

A currentless multiple switch open-circuit faults diagnosis strategy for modular multilevel converter with nearest level modulation in HVDC system

A Hybrid 1D-CNN-Bi-LSTM based Model with Spatial Dropout for Multiple Fault Diagnosis of Roller Bearing

Fault diagnosis of roller bearings is a crucial and challenging task to ensure the smooth functioning of modern industrial machinery under varying load conditions. Traditional fault diagnosis methods involve preprocessing of the vibration signals and manual feature extraction. This requires domain expertise and experience in extracting relevant features to accurately detect the fault. Hence, it is of great significance to implement an intelligent fault diagnosis method that involves appropriate automatic feature learning and fault identification. Recent research has shown that deep learning is an effective technique for fault diagnosis. In this paper, a hybrid model based on 1D-CNN (One-Dimensional Convolution Neural Networks) with Bi-LSTM (Bi-directional Long-Short Term Memory) is proposed to classify 12 different fault types. Firstly, vibration signals are given as input to 1D-CNN to extract intrinsic features from the input signals. Then, the extracted features are fed into a Bi-LSTM model to identify the faults. The performance of the proposed method is enhanced by applying Softsign activation function in the Bi-LSTM layer and Spatial Dropout in the neural network. To analyze the effectiveness of the proposed method, Case Western Reserve University (CWRU) bearing data is considered for experimentation. The results demonstrated that the proposed model has attained an accuracy of 99.84% in classifying the various faults. The superiority of the proposed method is verified by comparing the predictive accuracy of the proposed method with the existing fault diagnosis methods.