Dynamic safety assessment of a nonlinear pumped-storage generating system in a transient process

This paper focuses on a pumped-storage generating system with a reversible Francis turbine and presents an innovative framework for safety assessment in an attempt to overcome their limitations. Thus the aim is to analyze the dynamic safety process and risk probability of the above nonlinear generating system. This study is carried out based on an existing pumped-storage power station. In this paper we show the dynamic safety evaluation process and risk probability of the nonlinear generating system using Fisher discriminant method. A comparison analysis for the safety assessment is performed between two different closing laws, namely the separate mode only to include a guide vane and the linkage mode that includes a guide vane and a ball valve. We find that the most unfavorable condition of the generating system occurs in the final stage of the load rejection transient process. It is also demonstrated that there is no risk to the generating system with the linkage mode but the risk probability of the separate mode is 6 percent. The results obtained are in good agreement with the actual operation of hydropower stations. The developed framework may not only be adopted for the applications of the pumped-storage generating system with a reversible Francis turbine but serves as the basis for the safety assessment of various engineering applications.National Natural Science Foundation of ChinaFundamental Research Funds for the Central UniversitiesScientific research funds of Northwest A&F UniversityScience Fund for Excellent Young Scholars from Northwest A&F University and Shaanxi Nova progra

A fault diagnosis model based on singular value manifold features, optimized SVMs and multi-sensor information fusion

To achieve better fault diagnosis of rotating machinery, this paper presents a novel intelligent fault diagnosis model based on singular value manifold features (SVMF), optimized support vector machines (SVMs) and multi-sensor information fusion. Firstly, a new fault feature named SVMF is developed to better represent faults. SVMF is acquired by extracting manifold topology features of the singular spectrum. Compared with frequently-used fault features, the feature scale of SVMF is constant for variable rotating speed, and the extraction process of SVMF also has the effect of self-weighting. So SVMF has a better representation of faults. Then, to select optimal parameters for model training of SVMs, an improved fruit fly algorithm is proposed by introducing a guidance search mechanism and enhanced local search operation, and as a result both the convergence speed and accuracy are improved. Finally, the Dempster–Shafer evidence theory is introduced to fuse decision-level information from SVM models of multiple sensors. Information fusion eliminates the conflict of conclusions on fault diagnosis from multiple sensors, which leads to high robustness and accuracy of the fault diagnosis model. As a summary, the proposed method combines the advantages of SVMF in fault representation, SVMs in fault identification and the Dempster–Shafer evidence theory in information fusion, and as a result the proposed method will perform better at fault diagnosis. The proposed intelligent fault diagnosis model is subsequently applied to fault diagnosis of the gearbox. Experimental results show that the proposed diagnostic framework is versatile at detecting faults accurately

Information Theory and Its Application in Machine Condition Monitoring

Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries

Three-stage Hybrid Fault Diagnosis for Rolling Bearings with Compressively-sampled data and Subspace Learning Techniques

To avoid the burden of much storage requirements and processing time, this paper proposes a three-stage hybrid method, Compressive Sampling with Correlated Principal and Discriminant Components (CSCPDC), for bearing faults diagnosis based on compressed measurements. In the first stage, Compressive Sampling (CS) is utilised to obtain compressively-sampled signals from raw vibration data. In the second stage, an effective multi-step feature learning algorithm obtains fewer features from correlated principal and discriminant attributes from the compressively-sampled signals, which are then concatenated to increase the performance. In the third stage, with these concatenated features, Multi-class Support Vector Machine (SVM) is used to train, validate, and classify bearing faults. Results show that the proposed method, CS-CPDC, offers high classification accuracies, reduced computation time, and storage requirement, with fewer measurements.National Science Foundation of China; National Science Foundation of Shanghai

Diagnosis methodology for identifying gearbox wear based on statistical time feature reduction

Strategies for condition monitoring are relevant to improve the operation safety and to ensure the efficiency of all the equipment used in industrial applications. The feature selection and feature extraction are suitable processing stages considered in many condition monitoring schemes to obtain high performance. Aiming to address this issue, this work proposes a new diagnosis methodology based on a multi-stage feature reduction approach for identifying different levels of uniform wear in a gearbox. The proposed multi-stage feature reduction approach involves a feature selection and a feature extraction ensuring the proper application of a high-performance signal processing over a set of acquired measurements of vibration. The methodology is performed successively; first, the acquired vibration signals are characterized by calculating a set of statistical time-based features. Second, a feature selection is done by performing an analysis of the Fisher score. Third, a feature extraction is realized by means of the Linear Discriminant Analysis technique. Finally, fourth, the diagnosis of the considered faults is done by means of a Fuzzy-based classifier. The effectiveness and performance of the proposed diagnosis methodology is evaluated by considering a complete dataset of experimental test, making the proposed methodology suitable to be applied in industrial applications with power transmission systems.Peer ReviewedPostprint (published version

Fault Classification of Nonlinear Small Sample Data through Feature Sub-Space Neighbor Vote

The fault classification of a small sample of high dimension is challenging, especially for a nonlinear and non-Gaussian manufacturing process. In this paper, a similarity-based feature selection and sub-space neighbor vote method is proposed to solve this problem. To capture the dynamics, nonlinearity, and non-Gaussianity in the irregular time series data, high order spectral features, and fractal dimension features are extracted, selected, and stacked in a regular matrix. To address the problem of a small sample, all labeled fault data are used for similarity decisions for a specific fault type. The distances between the new data and all fault types are calculated in their feature subspaces. The new data are classified to the nearest fault type by majority probability voting of the distances. Meanwhile, the selected features, from respective measured variables, indicate the cause of the fault. The proposed method is evaluated on a publicly available benchmark of a real semiconductor etching dataset. It is demonstrated that by using the high order spectral features and fractal dimensionality features, the proposed method can achieve more than 84% fault recognition accuracy. The resulting feature subspace can be used to match any new fault data to the fingerprint feature subspace of each fault type, and hence can pinpoint the root cause of a fault in a manufacturing process

A Literature Review of Fault Diagnosis Based on Ensemble Learning

The accuracy of fault diagnosis is an important indicator to ensure the reliability of key equipment systems. Ensemble learning integrates different weak learning methods to obtain stronger learning and has achieved remarkable results in the field of fault diagnosis. This paper reviews the recent research on ensemble learning from both technical and field application perspectives. The paper summarizes 87 journals in recent web of science and other academic resources, with a total of 209 papers. It summarizes 78 different ensemble learning based fault diagnosis methods, involving 18 public datasets and more than 20 different equipment systems. In detail, the paper summarizes the accuracy rates, fault classification types, fault datasets, used data signals, learners (traditional machine learning or deep learning-based learners), ensemble learning methods (bagging, boosting, stacking and other ensemble models) of these fault diagnosis models. The paper uses accuracy of fault diagnosis as the main evaluation metrics supplemented by generalization and imbalanced data processing ability to evaluate the performance of those ensemble learning methods. The discussion and evaluation of these methods lead to valuable research references in identifying and developing appropriate intelligent fault diagnosis models for various equipment. This paper also discusses and explores the technical challenges, lessons learned from the review and future development directions in the field of ensemble learning based fault diagnosis and intelligent maintenance

Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion

Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts. In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.publishedVersio

Current Based Fault Detection and Diagnosis of Induction Motors. Adaptive Mixed-Residual Approach for Fault Detection and Diagnosis of Rotor, Stator, Bearing and Air-Gap Faults in Induction Motors Using a Fuzzy Logic Classifier with Voltage and Current Measurement only.

Induction motors (IM) find widespread use in modern industry and for this reason they have been subject to a significant amount of research interest in recent times. One particular aspect of this research is the fault detection and diagnosis (FDD) of induction motors for use in a condition based maintenance (CBM) strategy; by effectively tracking the condition of the motor, maintenance action need only be carried out when necessary. This type of maintenance strategy minimises maintenance costs and unplanned downtime. The benefits of an effective FDD for IM is clear and there have been numerous studies in this area but few which consider the problem in a practical sense with the aim of developing a single system that can be used to monitor motor condition under a range of different conditions, with different motor specifications and loads. This thesis aims to address some of these problems by developing a general FDD system for induction motor. The solution of this problem involved the development and testing of a new approach; the adaptive mixed-residual approach (AMRA). The main aim of the AMRA system is to avoid the vast majority of unplanned failures of the machine and therefore as opposed to tackling a single induction motor fault, the system is developed to detect all four of the most statistically prevalent induction motor fault types; rotor fault, stator fault, air-gap fault and bearing fault. The mixed-residual fault detection algorithm is used to detect these fault types which includes a combination of spectral and model-based techniques coupled with particle swarm optimisation (PSO) for automatic identification of motor parameters. The AMRA residuals are analysed by a fuzzy-logic classifier and the system requires only current and voltage inputs to operate. Validation results indicate that the system performs well under a range of load torques and different coupling methods proving it to have significant potential for use in industrial applications.The full-text was made available at the end of the embargo period on 29th Sept 2017