Deep model integrated with data correlation analysis for multiple intermittent faults diagnosis

This paper proposes a method based on chaotic spread spectrum sequence for synchronous online diagnosis of intermittent faults for complex and cable. networks, such as the aircraft power system. The novelties of the proposed method include: 1) since chaotic spread spectrum signals are sensitive to initial values, the number of chaotic sequences are not limited and the proposed method is not limited by the number of cables under monitoring; 2) with the self-correlation characteristic and cross-correlation characteristic of chaotic sequence, the proposed method can be extended to online diagnosis of multiple cable intermittent faults; and 3) the signal source impedance amplification method is proposed for intermittent arc faults of multiple cables, which improves the reflection coefficient and increases the accuracy of online detection and location of intermittent arc faults. A series of simulations and experiments on multiple cables with different intermittent fault modes are conducted to verify the effectiveness of the proposed approaches.

The problem of robust model-based diagnosis of process faults is addressed by means of artificial neural networks. Different structures and learning methods are investigated for both approaches to function approximation and pattern recognition. Main emphasis is placed upon static and dynamic neural nets that are used as predictors of nonlinear models for symptom generation. Dynamic neural networks are properly integrated into a generalized observer scheme. The goal is to achieve an adequate approximation of process outputs for each known class of system behavior. Symptoms are then evaluated by means of pattern classification. Application to a laboratory process is presented. A diagnosing subsystem is designed to detect incipient faults in the components of a three-tank system. It is implemented in real-time by using the SIMULINL/MATLAB programming environment. Experimental results regarding the diagnosis of single and multiple faults are included in a comparative study. It demonstrates the effectiveness of the suggested approach.

Aiming at solving the multiple fault diagnosis problem as well as the sequence of all the potential multiple faults simultaneously, a new multiple fault diagnosis method based on the dependency model method as well as the knowledge in test results and the prior probability of each fault type is proposed. Firstly, the dependency model of the system can be built and used to formulate the fault-test dependency matrix. Then, the dependency matrix is simplified according to the knowledge in the test results of the system. After that, the logic ‘OR’ operation is performed on the feature vectors of the fault status in the simplified dependency matrix to formulate the multiple fault dependency matrix. Finally, fault diagnosis is based on the multiple fault dependency matrix and the ranking of each fault type calculated basing on the prior probability of each fault status. An illustrative numerical example and a case study are presented to verify the effectiveness and superiority of the proposed method.

Fault diagnosis of rotating machines is an important task to prevent machinery downtime, and provide verifiable support for condition-based maintenance (CBM) decision-making. Deep learning-enabled fault diagnosis operations have become increasingly popular because features are extracted and selected automatically. However, it is challenging for these models to give superior results with rotating machine components of different scales, single and multiple faults across different rotating components, diverse operating speeds, and diverse load conditions. To address these challenges, this paper proposes a comprehensive learning approach with optimized signal processing transforms for single as well as multiple faults diagnosis across dissimilar rotating machine components: gearbox, bearing, and shaft. The optimized bicoherence, spectral kurtosis and cyclic spectral coherence feature spaces, and deep blending ensemble learning are explored for multiple faults diagnosis of these components. The performance analysis of the proposed approach has been demonstrated through a single joint training of the entire framework on a compound dataset containing multiple faults derived from three public repositories. A comparison with the state-of-the-art approaches that used these datasets, shows that our method gives improved results with different components and faults with nominal retraining.

Multiple faults are easily confused with single faults. In order to identify multiple faults more accurately, a highly efficient learning method is proposed based on a double parallel two-hidden-layer extreme learning machine, called DPT-ELM. The DPT-ELM method is a variant of an extreme learning machine (ELM). There are some issues with ELM. First, achieving a high accuracy requires too many hidden nodes; second, the direct connection between the input layer and the output layer is ignored. Accordingly, to deal with the above-mentioned problems, DPT-ELM extends the single-hidden-layer ELM to a two-hidden-layer ELM, which can achieve a desired performance with fewer hidden nodes. In addition, a direct connection is built between the input layer and the output layer. Since the input layer weights and the thresholds of the two hidden layers are determined randomly, this simplifies the improved model and shortens the calculation time. Additionally, to improve the signal to noise ratio (SNR), an adaptive waveform decomposition (AWD) algorithm is used to denoise the vibration signal. Then, the denoised signal is used to extract the eigenvalues by the time-domain and frequency-domain methods. Finally, the eigenvalues are input to the DPT-ELM classifier. In this paper, two groups of rolling bearing data at different speeds, which were collected from a real experimental platform, are used to test the method. Each set of data includes three single fault states, two complex fault states and a healthy state. The experimental results demonstrate that the DPT-ELM method achieves fast learning speed and a high accuracy. Moreover, based on 10-fold cross-validation, it proves to be an effective method to improve the accuracy with fewer hidden nodes.

Induction motors operate in difficult environments in the industry. Monitoring the performance of motors in such circumstances is significant, which can provide a reliable operation system. This paper intends to develop a new model for fault diagnosis based on the knowledge of transfer learning using the ImageNet dataset. The development of this framework provides a novel technique for the diagnosis of single and multiple induction motor faults. A transfer learning model based on a VGG-19 convolutional neural network (CNN) was implemented, which provided a quick and fast training process with higher accuracy. Thermal images with different induction motor conditions were captured with the help of an FLIR camera and applied as inputs to investigate the proposed model. The implementation of this task involved the use of a VGG-19 CNN-based pre-trained network, which provides autonomous features learning based on minimum human intervention. Next, a dense-connected classifier was applied to predict the true class. The experimental results confirmed the robustness and reliability of the developed technique, which was successfully able to classify the induction motor faults, achieving a classification accuracy of 99.8%. The use of a VGG-19 network allowed the attributes to be automatically extracted and associated with the decision-making part. Furthermore, this model was further compared with other applications based on related topics; it successfully proved its superiority and robustness.

With the development of the processing capacity of the embedded chip, it is possible to implement a machine learning algorithm in the embedded system. To achieve the fault status without poor portability, tricky threshold selection, and complex rulemaking, this article proposes a multimodal deep residual filter network (DRFN) for online multiple open-switch fault diagnosis of T-type three-level inverters. It contains low-rank matrix fusion (LMF), a DRFN, and a cross-transformer mechanism. The LMF fuses the voltage signal and the current signal for obtaining the unified representation. Then, the DRFN filters noise adaptively and extracts information effectively. Finally, the cross-transformer mechanism outputs the fault state of the T-type three-level inverter. The datasets consist of the dc-link voltage and load side current of the inverter control system. The data time window is selected as 20 ms. Through the real-time calculation of online monitored data, the experimental results show the effectiveness of the proposed fault diagnosis approach. Moreover, the accuracy of fault diagnosis is 99.18%, and the average open-circuit fault diagnosis time is 21 ms.

Process fault detection and diagnosis using neural networks—I. steady-state processes

Multiple fault diagnosis is a challenging problem because the number of candidates grows exponentially in the number of faults. The multiple fault problems is important, since the single fault assumption can lead to incorrect or failed diagnoses when multiple faults occur. In this work, we present an approach for diagnosing multi faults based on model using techniques of detection and localization. We use an observer to generate residuals for a decision in a stage of monitoring and diagnostic system when disruptions or defects occur. Our contribution is the proposal of a diagnostic method when multiple faults type actuators or sensors affect the system.

The diagnostic study on single-fault with distinguishing features based on monitoring data analysis is mature and fruitful in recent years. However, the early fault signals collected by practical monitoring systems often possess the following characteristics: 1) Fairly weak signal strength; 2) Submerged in powerful background noise; 3) Coupling of different fault data. These features not only increase the diagnostic difficulty, but also make the existing methods hardly to get the desired results. Consequently, the early compound faults diagnosis commonly in industrial systems is still a thorny and urgent problem. Therefore, in order to solve this problem and provide technical support for the practical industrial machinery fault diagnosis, a denoising-integrated sparse autoencoder (DISAE) model for early compound faults diagnosis is proposed in this paper. The innovation points of this study mainly include: 1) A feature-enhanced and denoising solution based on fault sensitivity degree (FSD) is designed, and the reconstructed diagnostic signals are acquired. 2) A disassociation framework is formulated, and the data coupling is solved. 3) A weight constraint term of SAE is constructed to improve the effectiveness and diversity of feature learning. 4) An adaptive loss function and a DISAE model is formed, and the early compound faults diagnosis is achieved. Finally, different trials and comparison results display the effectiveness and superiority of the designed DISAE based scheme.

In a chiller plant, primary or critical sensors are used to control the system operation while secondary sensors are installed to monitor the performance/health of individual equipment. Current sensor fault detection and diagnosis (SFDD) approaches are not applicable to secondary sensors which usually are not involved in the system control. Consequently, a hybrid multiple sensor fault detection, diagnosis and reconstruction (HMSFDDR) algorithm for chiller plants was developed. Machine learning and pattern recognition were used to predict the primary sensor faults through the comparison of the weekly performance curves. With the primary sensor signals reconstructed, the secondary sensor faults were estimated based on mass and energy balance. By applying the algorithm with various logged plant data and comparison with site checking results, a maximum of 75% effectiveness could be achieved. The merits of the present approach were further justified through off-site sensor testing which reinforced the usefulness of proposed HMSFDDR algorithm.

Dynamic output feedback-based fault-tolerant control design for T-S fuzzy systems with model uncertainties

A formal specification for analysing and implementing multiple fault diagnosis software is proposed in this paper. The specification computes all potential fault sources that correspond to a set of triggered alarms for a safety-related system, or part of a system. The detection of faults occurring in a safety-related system is a fundamental function that needs to be addressed efficiently. Safety monitors for fault diagnosis have been extensively studied in areas such as aircraft systems and chemical industries. With the introduction of intelligent sensors, diagnosis results are made available to monitoring systems and operators. For complex systems composed of thousands of components and sensors, the diagnosis of multiple faults and the computational burden of processing test results are substantial. This paper addresses the multiple fault diagnosis problem for zero-time propagation using a fault propagation graph. Components represented as nodes in a fault propagation graph are allocated with alarms. When faults occur and are propagated some of these alarms are triggered. The allocation of alarms to nodes is based on a severity analysis performed using a form of failure mode and effect analysis on components in the system.

The expert system for fault diagnosis based on classified hierarchical model is addressed so as to improve the maintenance capability of engineering vehicles under complex battle environment. The analysis of fault feature, fault pattern and failure mechanism of the whole machine and its electrical system is accomplished. Furthermore, the structure of classified hierarchical diagnosis model and method of knowledge representation are implemented so that the knowledge base of the fault diagnosis system can be developed. The forward reasoning mechanism of the fault diagnosis expert system is bring forward based on neural network intelligent reasoning that can achieve fuzzy input and multiple fault diagnosis. This work effectively increases the fault maintenance, diagnosis and support capabilities of the military troops.

Availability and continuous operation under critical conditions are very important in electric machine drive systems. Such systems may suffer from several types of failures that affect the electric machine or the associated voltage source inverter. Therefore, fault diagnosis and fault tolerance are highly required. This paper presents a new robust deep learning-based approach to diagnose multiple open-circuit faults in three-phase, two-level voltage source inverters for induction-motor drive applications. The proposed approach uses fault-diagnosis variables obtained from the sigmoid transformation of the motor stator currents. The open-circuit fault-diagnosis variables are then introduced to a bidirectional long short-term memory algorithm to detect the faulty switch(es). Several simulation and experimental results are presented to show the proposed fault-diagnosis algorithm’s effectiveness and robustness.