Fault Diagnosis Algorithm Research Articles

An open source experimental framework and public dataset for vibration-based fault diagnosis of electrical submersible pumps used on offshore oil exploration

An Electrical Submersible Pump (ESP) is an important equipment used in the industry for lifting liquids in various types of wells. An ESP is widely used in the oil industry for offshore exploration. Detecting a faulty ESP before installation is a predictive maintenance measure in order to extend its operational time. Machine learning fault diagnosis is an effective way for performing this task. Machine learning fault diagnosis algorithms are highly dependent of the availability of an appropriate problem dataset. This paper describes in detail the problem of ESP fault diagnosis and the ESPset dataset, a real-world and public dataset for vibration-based fault diagnosis of electrical submersible pumps used on offshore oil exploration. In addition, the paper also proposes an experimental framework for adequately comparing research works based on the ESPset dataset and defines benchmark classifiers and respective results as referential to the fault diagnosis research community. The framework considers the fact that some subset of samples are not drawn independently, and therefore, proposes a cross-validation sampling strategy that mitigates the similarity bias among samples. Indeed, this work shows that a conventional k-fold cross-validation may lead to a clear overestimation of the average performance. This fact is supported by results which show that the best classification model drops from a mean F-measure of 0.887 to 0.733 when removing the similarity bias from the data.

Research on motor rotation anomaly detection based on improved VMD algorithm

PurposeThe electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.Design/methodology/approachThe VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.FindingsThis paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.Originality/valueBy using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

System Resilience through Health Monitoring and Reconfiguration

We demonstrate an end-to-end framework to improve the resilience of man-made systems to unforeseen events. The framework is based on a physics-based digital twin model and three modules tasked with real-time fault diagnosis, prognostics and reconfiguration. The fault diagnosis module uses model-based diagnosis algorithms to detect and isolate faults and generates interventions in the system to disambiguate uncertain diagnosis solutions. We scale up the fault diagnosis algorithm to the required real-time performance through the use of parallelization and surrogate models of the physics-based digital twin. The prognostics module tracks fault progression and trains the online degradation models to compute remaining useful life of system components. In addition, we use the degradation models to assess the impact of the fault progression on the operational requirements. The reconfiguration module uses PDDL-based planning endowed with semantic attachments to adjust the system controls to minimize the fault impact on the system operation. We define a resilience metric and use a fuel system example to demonstrate how the metric improves with our framework.

A Fault Diagnosis Method for 5G Cellular Networks Based on Knowledge and Data Fusion.

As 5G networks become more complex and heterogeneous, the difficulty of network operation and maintenance forces mobile operators to find new strategies to stay competitive. However, most existing network fault diagnosis methods rely on manual testing and time stacking, which suffer from long optimization cycles and high resource consumption. Therefore, we herein propose a knowledge- and data-fusion-based fault diagnosis algorithm for 5G cellular networks from the perspective of big data and artificial intelligence. The algorithm uses a generative adversarial network (GAN) to expand the data set collected from real network scenarios to balance the number of samples under different network fault categories. In the process of fault diagnosis, a naive Bayesian model (NBM) combined with domain expert knowledge is firstly used to pre-diagnose the expanded data set and generate a topological association graph between the data with solid engineering significance and interpretability. Then, as the pre-diagnostic prior knowledge, the topological association graph is fed into the graph convolutional neural network (GCN) model simultaneously with the training data set for model training. We use a data set collected by Minimization of Drive Tests under real network scenarios in Lu'an City, Anhui Province, in August 2019. The simulation results demonstrate that the algorithm outperforms other traditional models in fault detection and diagnosis tasks, achieving an accuracy of 90.56% and a macro F1 score of 88.41%.

Supervised machine learning-based salp swarm algorithm for fault diagnosis of photovoltaic systems

The diagnosis of faults in grid-connected photovoltaic (GCPV) systems is a challenging task due to their complex nature and the high similarity between faults. To address this issue, we propose a wrapper approach called the salp swarm algorithm (SSA) for feature selection. The main objective of SSA is to extract only the most important features from the raw data and eliminate unnecessary ones to improve the classification accuracy of supervised machine learning (SML) classifiers. Subsequently, the selected features are used to train supervised machine learning (SML) techniques in distinguishing between various operating modes. To evaluate the efficiency of the technique, we used healthy and faulty data from GCPV systems that have been injected with frequent faults, 20 different types of faults were introduced, including line-to-line, line-to-ground, connectivity faults, and those affecting the operation of bay-pass diodes. These faults present diverse conditions, such as simple and multiple faults in the PV arrays and mixed faults in both arrays. The performances of the developed SSA-SML are compared with those using principal component analysis (PCA) and kernel PCA (KPCA) based SML techniques through different criteria (i.e., accuracy, recall, precision, F1 score, and computation time). The experimental findings demonstrated that the proposed diagnosis paradigm outperformed the other techniques and achieved a high diagnostic accuracy (an average accuracy greater than 99%) while significantly reducing computation time.

Research on operation fault diagnosis algorithm of power grid equipment based on power big data

Research on operation fault diagnosis algorithm of power grid equipment based on power big data

Fault detection and diagnosis using the dynamic network framework

A local model-based method for fault detection and diagnosis (FDD) in large-scale interconnected network systems is introduced, using models in a dynamic network framework. To this end, model validation methods are developed for validating single modules in a dynamic network, which are generalized from the classical auto- and cross-correlation tests for open- and closed-loop systems. Invalidation of the model can indicate the detection of a fault in the system. A fault diagnosis algorithm is developed that includes fault isolation and optimal placement of external excitation signals. Numerical illustrations demonstrate the method’s capability to detect a fault in a local module and isolate it within the entire network system.

Machine learning models for condition-based maintenance with regular truncated signals

Condition-based maintenance (CBM) of industrial machines depends on the continuous, real-time monitoring of the machine’s operational condition via smart sensors attached to different components on the machine. The problem of regularly spaced missing data, which can occur due to a variety of hardware or software issues, is one that is often overlooked in the literature surrounding CBM in industrial machines. Such missing data can cause issues in interpreting the true operational state of the machine, which can reduce the effectiveness of CBM processes. In this paper, we examine the capabilities of five data imputation techniques for handling this regular missing data and examine the impact these techniques have on machine learning (ML) classification algorithms for machine fault diagnosis. We examine the following techniques: simple mean imputation, mean imputation with outliers removed, best and worst-case imputation, and previous day imputation. Each of these methods is configured with the specific parameters that they will only consider data from the previous 24 hours, to ensure that the data is recent, and adequately represents the current status of the machine. The efficacy of each method at accurately reconstructing the missing data and the impact they have on ML classification is recorded in the results. The models are evaluated on a real-world dataset and are evaluated on a variety of common performance metrics.

A classification and prediction model with the sparrow search-probabilistic neural network algorithm for transformer fault diagnosis

A classification and prediction model with the sparrow search-probabilistic neural network algorithm for transformer fault diagnosis

Optimized LSTM based on an improved sparrow search algorithm for power battery fault diagnosis in new energy vehicles

Optimized LSTM based on an improved sparrow search algorithm for power battery fault diagnosis in new energy vehicles

Comparative analysis of the performance of supervised learning algorithms for photovoltaic system fault diagnosis

New trends were introduced in using PhotoVoltaic (PV) energy which are mostly attributable to new laws internationally having a goal to decrease the usage of fossil fuels. The PV systems efficiency is impacted significantly by environmental factors and different faults occurrence. These faults if they were not rapidly identified and fixed may cause dangerous consequences. A lot of methods have been introduced in the literature to detect faults that may occur in a PV system such as using Current-Voltage (I-V) curve measurements, atmospheric models and statistical methods. In this paper, various machine learning techniques in particular supervised learning techniques are used for PV array failure diagnosis. The main target is the identification and categorization of several faults that may occur such as shadowing, degradation, open circuit and short circuit faults that have a great impact on PV systems performance. The results showed the technique’s high ability of fault diagnosis capability. The K-Nearest Neighbor (KNN) technique showed the best fault prediction performance. It achieves prediction accuracy of 99.2% and 99.7% Area Under Curve-Receiver Operating Curve (AUC-ROC) score. This shows its superiority in fault prediction in PV systems over other used methods Decision Tree, Naïve Bayes, and Logistic Regression.

Research on Fault Diagnosis and Prediction Algorithms for Power Equipment in Smart Grids

Research on Fault Diagnosis and Prediction Algorithms for Power Equipment in Smart Grids

Machine Learning based Drive Fault Diagnosis and Isolation Algorithm for 4WIS4WID Vehicle using Residual Visualization

Machine Learning based Drive Fault Diagnosis and Isolation Algorithm for 4WIS4WID Vehicle using Residual Visualization

Fault-tolerant Hamiltonian cycle strategy for fast node fault diagnosis based on PMC in data center networks.

System-level fault diagnosis model, namely, the PMC model, detects fault nodes only through the mutual testing of nodes in the system without physical equipment. In order to achieve server nodes fault diagnosis in large-scale data center networks (DCNs), the traditional algorithm based on the PMC model cannot meet the characteristics of high diagnosability, high accuracy and high efficiency due to its inability to ensure that the test nodes are fault-free. This paper first proposed a fault-tolerant Hamiltonian cycle fault diagnosis (FHFD) algorithm, which tests nodes in the order of the Hamiltonian cycle to ensure that the test nodes are faultless. In order to improve testing efficiency, a hierarchical diagnosis mechanism was further proposed, which recursively divides high scale structures into a large number of low scale structures based on the recursive structure characteristics of DCNs. Additionally, we proved that $ 2(n-2){n^{k-1}} $ and $ (n-2){t_{n, k}}/{t_{n, 1}} $ faulty nodes could be detected for $ BCub{e_{n, k}} $ and $ DCel{l_{n, k}} $ within a limited time for the proposed diagnosis strategy. Simulation experiments have also shown that our proposed strategy has improved the diagnosability and test efficiency dramatically.

Self-Healing Photovoltaic Microconverter With Zero Redundancy and Accurate Low-Cost Fault Detection

This paper studies a zero-redundancy fault-tolerant dc-dc series resonant converter (ZR-SRC) for residential photovoltaic (PV) applications. This converter can handle faulty conditions on-the-fly using topology morphing control without increasing the number of components. Furthermore, it can withstand a single fault in both input and output bridge semiconductors, i. e., up to two semiconductor faults. The proposed dc-dc converter could increase the availability of residential photovoltaic-powered dc microgrids. Comprehensive fault diagnosis and localization algorithms are proposed for MOSFETs on both sides of the ZR-SRC. The paper proposes а short-circuit fault (SCF) detection in the input-side MOSFETs using the existing PV voltage measurements, decreasing the converter cost and size. The open-circuit fault (OCF) in the output-side MOSFETs is detected by sensing the voltage of the bottom two switches sharing the same ground with the dc bus. In addition, the post-fault operation of the converter is thoroughly analyzed. Recommendations for the PV power clipping are given to ensure the safe operation of the converter after a fault. A 300 W experimental setup is built to demonstrate the feasibility and performance of the proposed fault-tolerant converter.

Learning System Descriptions for Cyber-Physical Systems

Fault diagnosis algorithms compute faulty components by comparing actual observations against some model of known behaviour. A major challenge for fault diagnosis lies in creating such a suitable model. In the past, models were usually assumed to be given by experts. But in modern cyber-physical systems this assumption cannot be held, as experts are expensive and system architectures may be subject to change. This article presents a novel algorithm to obtain those models automatically and apply them for fault diagnosis. The evaluation was done on the Tennessee Eastman process and on two benchmarks of multiple-tank systems.

Fault diagnosis algorithm for dry-type transformer based on deep learning of small-sample acoustic array signals

Fault diagnosis algorithm for dry-type transformer based on deep learning of small-sample acoustic array signals

Fault Diagnosis Algorithm for Pumping Unit Based on Dual-Branch Time–Frequency Fusion

Fault Diagnosis Algorithm for Pumping Unit Based on Dual-Branch Time–Frequency Fusion

Review on Battery State Estimation and Management Solutions for Next-Generation Connected Vehicles

The transport sector is tackling the challenge of reducing vehicle pollutant emissions and carbon footprints by means of a shift to electrified powertrains, i.e., battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs). However, electrified vehicles pose new issues associated with the design and energy management for the efficient use of onboard energy storage systems (ESSs). Thus, strong attention should be devoted to ensuring the safety and efficient operation of the ESSs. In this framework, a dedicated battery management system (BMS) is required to contemporaneously optimize the battery’s state of charge (SoC) and to increase the battery’s lifespan through tight control of its state of health (SoH). Despite the advancements in the modern onboard BMS, more detailed data-driven algorithms for SoC, SoH, and fault diagnosis cannot be implemented due to limited computing capabilities. To overcome such limitations, the conceptualization and/or implementation of BMS in-cloud applications are under investigation. The present study hence aims to produce a new and comprehensive review of the advancements in battery management solutions in terms of functionality, usability, and drawbacks, with specific attention to cloud-based BMS solutions as well as SoC and SoH prediction and estimation. Current gaps and challenges are addressed considering V2X connectivity to fully exploit the latest cloud-based solutions.

Customized Research on Fault Diagnosis of Civil Aircraft Onboard Maintenance System Based on Big Data Technology

Abstract This paper dissects the BP neural network and RNN on the basis of existing research, obtains the improved long and short-term memory network, introduces the attention mechanism into it, and designs the fault diagnosis model of airborne maintenance system based on AM-LITM with big data technology. Focusing on the use of the BP learning algorithm to obtain the reference error of each unit needed to adjust the weight of each connection, real-time updating of information through the long- and short-term memory network to solve the problem of gradient dispersion of RNN, and at the same time, the method designed in this paper is compared with the traditional method from the aspects of fault diagnosis and prediction. The results show that the average prediction error of the traditional fault diagnosis algorithm is around 0.06, and it only has good diagnosis performance for the A-type fault, except for the classification accuracy rate of 0.9845. The rest of the performance index is 1. The diagnosis accuracy of the AM-LSTM fault diagnosis algorithm for three types of faults can reach 1, and all the indexes for the A, B, and C faults are 1, which shows that it greatly improves the performance of the traditional fault diagnosis algorithm. Diagnostic accuracy of civil aircraft on-board maintenance system faults is verified to verify the effectiveness and superiority of the designed model.