Multi Fault Research Articles

Electromechanical Modelling and Multi Faults Diagnosis of Straight Bevel Gear Pair with Experimental Validation

Electromechanical Modelling and Multi Faults Diagnosis of Straight Bevel Gear Pair with Experimental Validation

Open Heterogeneous Data for Condition Monitoring of Multi Faults in Rotating Machines Used in Different Operating Conditions

Rotating machines are widely used in several fields such as railways, renewable energies, robotics, etc. This diversity of application implies a large variety of faults of critical components susceptible to fail. For this purpose, prognostics and health management (PHM) is deployed to effectively monitor these components through the detection, diagnostics as well as prognostics of faults. In the literature, there exist numerous methods to ensure the above monitoring activities. However, few of them consider different failure types using heterogeneous data and various operating conditions. Also, there are no dominant methods that can be generalized for monitoring. For this reason, the genericity of these methods and their applicability in several systems is a crucial issue. To help researchers to achieve the above challenges, this paper presents a detailed description of data sources from experimental test benches. These data-sets correspond to different case studies that monitor the health states of multiple critical components in various operating conditions using numerous sensors.

Testability design of memristive digital circuits based on Knowm memristor

A multi-function memristive logic test unit based on Knowm memristor is designed and implemented to the memristive digital circuits testability design in this article. The single fault memristive digital circuit is analyzed, Boolean difference algorithm is applied to the memristive digital circuits to generate test patterns and determine the location of the fault nodes. The multi-function memristive logic test unit can realize different functions, combining Boolean difference to solve the problem of uncertain faults with the same test patterns, and improve the controllability and observability of memristive digital circuits. It is a new exploration in testability design of memristive digital circuits, which provides new ideas and development directions, and lays a foundation for the research on the testability design of multi fault memristor digital circuits.

THE MATHEMATICAL ANALYSIS AND IMPROVEMENT OF MATRIX ALGORITHM FOR FEEDER FAULT LOCATION BASED ON FTU IN THE DISTRIBUTION NETWORK

The mathematical analysis and improvement of matrix algorithm based on FTU has been proposed to solve fault location problem in complex multi-source and multi fault distribution networks of power system. Based on the structural characteristics of the distribution system, a description matrix D is established by assuming the power sources and a positive direction and it derived from the correlation between the positive direction and different power lines. As a function of the fault current direction transmitted by the terminal power supply unit which is our feeder terminal unit (FTU), the fault information vector F is set up. By combining the description matrix D and searching for non-zero elements in the fault information vector, the fault location vector is discovered according to the fault location criteria. During the observation of the elements in the fault location vector, by analyzing we immediately found the location of the defect area. This algorithm can solve problems that cannot be completely solved by other algorithms, such as power terminal failures, ring network failures, and multi-source failures. This method does not require matrix multiplication or normalization. The widespread use of distributed generation (DG) has made the distribution network more complex, and it leads to the failure of the traditional matrix algorithm. Therefore, the matrix algorithm is further improved to adapt the complex characteristics of DG. Considering the accuracy, we avoid random search operations by filtering fault candidate scenarios based on fault confidence and Each algorithm run 100 times in a loop, and the average time taken for a single run is used as a measure of the computational efficiency of the algorithm. The matrix algorithm utilizes the information uploaded by FTU to SCADA to create network description matrix, fault information matrix, which are then used to obtain the fault judgment matrix for fault location and isolation and it proves that the algorithm judgment is effective.

A multi fault classification in a rotor-bearing system using machine learning approach

A multi fault classification in a rotor-bearing system using machine learning approach

Nonlinear dynamic response on multi-fault rod fastening rotor with variable parameters

A multi-fault nonlinear model of rod fastening rotor is proposed considering the structural properties of rod fastening and the integration of multiple factors. The nonlinear analysis is performed to present the nonlinear characteristics of gas turbine rotor system with multi faults. The fault dynamics of rod fastening rotor is exposed for interaction and failure mechanism of multiple faults. The variable parameter analysis reveals the impact of system parameters variation on vibration response in multi-fault rod fastening rotor system. Analysis results reflect that multi faults enhances the nonlinearity of the system and makes dynamic characteristics more complicated. Complex nonlinear characteristics of the dynamical system are presented under different system parameters. The multi-fault nonlinear dynamic model and the nonlinear analysis of variable parameter contribute to the fault identification and stability research in the rod fastening rotor of gas turbine, which will provide the reference for the design and application of rod fastening rotor system.

Research on multi-fault diagnosis method and test platform of main transmission Machinery of wind Turbine based on electrical signal

An optimised design was carried out based on the original semi-physical simulation system for the main drive chain of a wind turbine. The input of upper computer simulating wind condition, the safety control of electrical signal and vibration signal acquisition module, relay and AC contactor and dual motor control were added. Simultaneously, the simulation schemes of some common faults on the main drive of wind turbines were also designed in order to perform the experimental simulation of the single fault and multi fault. The primary purpose of simulation is to use practical approaches(such as FFT,EMD)to analyze the changes of the fault signals based on the collected electrical signals, and to determine whether there is a fault through comparison and theoretical analysis. In addition, it aimed for verifying the feasibility of electrical signals under multi-fault conditions, and discussing advantages of electrical signals over vibration signals.

Two earthquake events on the Pamsoa Segment of the Matano Fault, Sulawesi

Sulawesi Island crossed by the importance active faults of the Palukoro Fault in the western part and the Matano Fault in the eastern part. Pamsoa Segment, the fourth of six-segments of the Matano Fault (from west to east), located near importance Sorowako mining city, Sulawesi. Pamsoa segment has a 38 km length, oriented N295°E, and has an obvious 475 m sinistral river offset. A reconnaissance survey conduct in this segment along the fault crosses the mining dirt road. Two fault rupture existed on the Holocene river terrace. Accelerated Mass Spectrometry (AMS) carbon dating shows age about 5218 BP and 4446 BP on the soil samples. Nice slicken-side on red soil shows as a fresh fault rupture. This rupture shows a possible two earthquake event on the Pamsoa Segment. The Matano fault has potency similar to the 2018 Palu earthquake. If the fault has similar multi fault rupture earthquake characteristic, the Matano Fault is also classified as a Magnitude 7+ class earthquake. Further research is needed to get precise earthquake geology parameters and ideal charcoal samples.

Wind turbine fault diagnosis based on ReliefF-PCA and DNN

A large amount of data would be generated during the operation of wind turbine (WT), which is easy to cause dimensional disaster, and if more than one WT fault occur, multiple sensors would alarm. To solve the problems of big data, inaccurate and untimely fault diagnosis and so on, a hybrid fault diagnosis method is developed based on ReliefF, principal component analysis (PCA) and deep neural network (DNN) in this paper. Firstly, the ReliefF method is used to select the fault features and reduce the data dimensions. Secondly, PCA algorithm is used to further reduce the data dimensions, which is mainly used to reduce the redundancy among the data and improve the accuracy of fault diagnosis. Finally, the ReliefF-PCA-DNN model is constructed, optimized and used for the fault case of a wind farm in Jilin Province. The experimental results show that, for the single fault, the accuracies of the proposed hybrid models are all more than 98.5% and for the multi faults, the accuracy of the proposed model is more than 96%, which both are all much higher than the comparison methods. So, the method could diagnose the WT faults well.

Multi-fault diagnosis of rotating machinery based on deep convolution neural network and support vector machine

Because multi-fault vibration signals in rotating machinery are often more complicated than single faults, human-designed fault feature sets are not yet able to respond adequately to the complex task of fault diagnosis. To address this, a fault diagnosis method, based on deep convolution neural network (DCNN) and support vector machine (SVM), is proposed. The DCNN, a data-driven deep-learning model, is applied to extract the fault features automatically. The fault-feature information can be extracted adaptively according to the minute differences in local fault signals. First, the envelope spectrum obtained by Hilbert transform was fed into the DCNN to extract the fault features, and then combined with SVM (the classifier) to diagnose multi faults of bearings and rotors. Then, 16 frequency-domain features were extracted by the DCNN, and the average accuracy was found to be 90.29%, which showed that machine wisdom alone was insufficient for this task. Therefore, two additional time-domain features extracted by human experts were adopted together with the machine-designed features to create the method that we call the semi-DCNN method. The theoretical mechanism of the proposed method was clear: it utilized both machine wisdom and human experience in designing the fault features. The effectiveness of the new method was proved by the fact that average diagnostic accuracy increased to 98.71% during diagnosis of composite faults in N205 bearing and rotor.

Dynamic analysis and experiment investigation of a cracked dual-disc bearing-rotor system based on orbit morphological characteristics

The paper is aimed to examine dynamic behaviors of a dual-disc bearing-rotor system in multi-fault state, and the crack detection based on the orbit morphological characteristics and vibration responses is proposed. Dynamic response and vibration signal analysis are two significant studies in rotor system. Most researchers have simulated the nonlinear dynamics and analyzed the fault signal using various methods separately. However, the fault feature from vibration signal is tightly connected with the dynamic mechanism in the rotor system, especially in rotor system with coupling multi-fault. In the paper, the dynamic model of the dual-disc bearing-rotor system is established, which takes into account the effects of crack, rub-impact and nonlinear oil-film forces. The vibration responses and the effect of crack on dual-disc rotor system with multi faults are investigated. The existence of crack and the coupling effect of multi faults enrich dynamic behavior of the dual-disc bearing-rotor system, and the response near the 1/2 subcritical speed provides a criterion for crack detection. Experiment investigation is attempted for the first time, which is based on the changes of crack depth and rotation speed for multi-fault dual-disc rotor system. The analysis of the dynamic response and the orbit morphological characteristics from experiment can effectively detect the crack information.

Online Fault Diagnosis for Industrial Processes With Bayesian Network-Based Probabilistic Ensemble Learning Strategy

The efficient mitigation of the detrimental effects of a fault in complex systems requires online fault diagnosis techniques that are able to identify the cause of an observable anomaly. However, an individual diagnosis model can only acquire a limited diagnostic effect and may be insufficient for a particular application. In this paper, a Bayesian network-based probabilistic ensemble learning (PEL-BN) strategy is proposed to address the aforementioned issue. First, an ensemble index is proposed to evaluate the candidate diagnosis models in a probabilistic manner so that the diagnosis models with better diagnosis performance can be selected. Then, based on the selected classifiers, the architecture of the Bayesian network can be constructed using the proposed three types of basic topologies. Finally, the advantages of different diagnosis models are integrated using the developed Bayesian network, and thus, the fault causes of the observable anomaly can be accurately inferred. In addition, the proposed method can effectively capture the mixed fault characteristics of multifaults (MFs) by integrating decisions derived from different diagnosis models. Hence, this method can also provide a feasible solution for diagnosing MFs in real industrial processes. A simulation process and a real industrial process are adopted to verify the performance of the proposed method, and the experimental results illustrate that the proposed PEL-BN strategy improves the diagnosis performance of single faults and is a feasible solution for MF diagnosis. Note to Practitioners —The focus of this paper is to develop a probabilistic ensemble learning strategy based on the Bayesian network (PEL-BN) to diagnose different kinds of faults in industrial processes. The PEL-BN strategy can automatically select the base classifiers to establish the architecture of the Bayesian network. In this way, the conclusions of these base classifiers can be effectively integrated to provide better diagnosis performance. In addition, the proposed method is also a feasible technique for diagnosing MFs resulted from the joint effects of multiple faults.

Performance Improvement of Classifier in Fault Diagnosis of Rotating Machines Using Sensor Fusion Techniques

The shaft, rotor, bearing and gear are the important elements of the rotating machines. Most of the problems in rotating machines are caused due to bearings and shaft. The failure of rotating machine causes production downtime and economic & safety issues. Vibration signal analysis is highly accepted technique in fault diagnosis of rotating machine. For automation of fault diagnosis, machine learning approach has been followed. Machine learning classifies fault based on variation in signatures pattern of the machine. But its effectiveness gets reduced when it is used for multi fault class problem. So in the present work, sound signals are also used along with vibration signals for applying sensor fusion techniques. In sensor fusion, signals from various sensors are fused in three levels such as data fusion, feature fusion and decision level fusion and the fused data sets are used for fault classification using machine learning algorithm. The performance of each technique is studied in detail and compared using classification accuracy. A new method is proposed by combination of fusion techniques to enhance the performance.

A New Data Fusion Method for Multi Terminal Fault Location Based on Sensor Networks

Data fusion with different types of data that used for grid fault location can bring more accurate and clear results. Therefore, a new method for fault location which contains multi grid current and voltage, time and coordinate data information is proposed. Transient traveling wave is analyzed for determine the required data information in the first place, and then affirm the positioning principle and the selection rules of positioning network according to characteristics of multi fault traveling wave. Finally, Grid fault location will be precisely positioned by applying multiple data grid fault location algorithm. Simulation results shows that the proposed method is feasible, and the new algorithm has higher positioning accuracy.

Multi Fault Detection and Correction in Fault Tolerance Parallel Fats Using Bch Code

Multi Fault Detection and Correction in Fault Tolerance Parallel Fats Using Bch Code

Towards Diagnosing Cascading Outages in Cyber Physical Energy Systems using Temporal Causal Models

Cascading failures in critical cyber physical systems such as power systems are rare but lead to huge social and economic implications. Timely diagnosis of faults in these systems is a challenging task due to inherent heterogeneity and scale of the system. In the past, we have successfully demonstrated a robust technique for diagnosing independent component faults using Temporal Causal Diagrams (TCD) at sub-system level. In this paper, we present a systematic approach of using the sub-system level fault models to auto-generate a systemlevel fault model that helps in diagnosing cascading failures. We show the time complexity of our model generation algorithm using industry standard Power Transmission networks. Further, we describe the updates to the existing TCD reasoner algorithms and report the TCD diagnosis results for simulated multi fault scenario on a standard power system.

A new support vector data description method for machinery fault diagnosis with unbalanced datasets

In machinery fault diagnosis area, the obtained data samples under faulty conditions are usually far less than those under normal condition, resulting in unbalanced dataset issue. The commonly used machine learning techniques including Neural Network, Support Vector Machine, and Fuzzy C-Means, etc. are subject to high misclassification with unbalanced datasets. On the other hand, Support Vector Data Description is suitable for unbalanced datasets, but it is limited for only two class classification. To address the aforementioned issues, Support Vector Data Description based machine learning model is formulated with Binary Tree for multi-classification problems (e.g. multi fault classification or fault severity recognition, etc.) in machinery fault diagnosis. The binary tree structure of multiple clusters is firstly drawn based on the order of cluster-to-cluster distances calculated by Mahalanobis distance. Support Vector Data Description model is then applied to Binary Tree structure from top to bottom for classification. The parameters of Support Vector Data Description are optimized by Particle Swarm Optimization algorithm taking the recognition accuracy as objective function. The effectiveness of presented method is validated in the rotor unbalance severity classification, and the presented method yields higher classification accuracy comparing with conventional models.

Combining inertia and constriction technique in the PSO applied to fault identification in a hydraulic system

A novel methodology for simultaneous parametric fault isolation and mode switching identification in the spool motion of a Directional Control Valve (DCV), under multi fault assumption, has been reported in this paper. The shape of the profile traversed by the DCV spool was assumed to be trapezoidal in both healthy and faulty condition, but the slope of trapezoidal may change due to fault. Under this assumption, the real valued fault parameter and binary mode switching were identified by real valued Particle Swarm Optimization (PSO) alone instead of a combined real and binary valued PSO (Hybrid PSO). A novel PSO algorithm by combining the concepts of varying inertia weight (both increasing and decreasing trend) and constriction factor has been proposed in the article to achieve more accurate identification. Its validity was examined using an existing heuristic formula and by conducting several tests on a benchmark function used for fault identification. Superior improvement was observed in the identification with increasing inertia weight than that of widely used decreasing inertia weight, when combined with the constriction coefficient. A high pressure hydraulic circuit used in dumper and several other lifting machines was used as a simulation example.

Fast fault detection method for modular multilevel converter semiconductor power switches

This study proposes a new fault detection method for modular multilevel converter (MMC) semiconductor power switches. While in common MMCs, the cells capacitor voltages are measured directly for control purposes, in this study voltage measurement point changes to the cell output terminal improving fault diagnosis ability. Based on this measurement reconfiguration, a novel fault detection algorithm is designed for MMCs semiconductor power switches. The open circuit and short circuit faults are detected based on unconformity between modules output voltage and switching signals. Simulation and experimental results confirm accurate and fast operation of the proposed method in faulty cell diagnosis. Implementation simplicity, no need to extra voltage measurements and multi faults detection ability are some other advantages of the proposed method.

FE Analysis on Contact Properties between Root- Blade and Slot-Disc

The contact problem between root-blade and slot-disk structure of compressor is of much importance, because there is serious stress concentration in the structure, which is one of the parts with multi faults in aero-engine. First, finite element model for dovetail attachment is established to analyze stress concentration effects. Then, the stress distribution and other parameters such as contact pressure is obtained through contact analysis. In this paper, the finite element method based on contact analysis is used. Calculating results show that equivalent stress, contact pressure of blade-disk structure increase steadily as the rotating speed increases.