High-Speed Train Traction System Reliability Analysis

As the core power unit of high-speed train (HST), the diagnosis of faults in traction motor system has a significant importance on both safety and reliability, which can avoid HST crashes. According to the current problems such as early fault characteristics are not obvious and tight coupling, the reliable model with fault severity analysis and the required diagnosis accuracy cannot be achieved by current techniques. Therefore, it is crucial to evaluate HST reliability through resilience enhancement strategies to ensure it can operate with higher resilience. This chapter proposes a method for evaluating the overall reliability of HST traction motors associated with the idea of system modeling and machine learning techniques. First, a novel fault severity model is proposed suitable for the normal and fault conditions. Then electromagnetic torque energy entropy coding is utilized to extract fault features and construct different feature matrixes. Resilience enhancement strategies with support vector machine models are generated from a novel gray wolf optimizer algorithm. The performance of the proposed work is validated through simulation and experimentation on a fault-testing verification platform for the HST traction system

Fault tolerance in super-scalar and VLIW processors

In this paper, we present a method for utilizing the spare capacity in super-scalar and very long instruction word (VLIW) processors to tolerate functional unit failures. Unlike previous work that was primarily interested in detection of transient faults, we are concerned with more permanent and/or intermittent faults which necessitate processor reconfiguration. Our method utilizes the VLIW compiler or the superscalar scheduler to insert redundant operations whenever idle functional units exist. The results of these redundant operations are used to detect and diagnose functional unit failures. For super-scalar processors, the scheduler can then utilize this information to ensure that operations are performed only on non-faulty units. In VLIW processors, this is equivalent to recompiling the code to run on the remaining non-faulty functional units. Since in certain applications, recompilation may not be possible, we consider two alternative reconfiguration strategies for VLIW processors. These strategies sacrifice storage space and execution time, respectively, in order to reconfigure without recompiling. We present Markov models that describe the behavior of processors using these different approaches and we evaluate their reliabilities. The results show that, while super-scalar and VLIW with recompilation provide the highest reliability, all proposed strategies significantly increase reliability over that of an unprotected processor

Enhanced Industrial Machinery Condition Monitoring Methodology based on Novelty Detection and Multi-Modal Analysis

This paper presents a condition-based monitoring methodology based on novelty detection applied to industrial machinery. The proposed approach includes both, the classical classification of multiple a priori known scenarios, and the innovative detection capability of new operating modes not previously available. The development of condition-based monitoring methodologies considering the isolation capabilities of unexpected scenarios represents, nowadays, a trending topic able to answer the demanding requirements of the future industrial processes monitoring systems. First, the method is based on the temporal segmentation of the available physical magnitudes, and the estimation of a set of time-based statistical features. Then, a double feature reduction stage based on Principal Component Analysis and Linear Discriminant Analysis is applied in order to optimize the classification and novelty detection performances. The posterior combination of a Feed-forward Neural Network and One-Class Support Vector Machine allows the proper interpretation of known and unknown operating conditions. The effectiveness of this novel condition monitoring scheme has been verified by experimental results obtained from an automotive industry machine.Postprint (published version

A New Implementation of Digital Twins for Fault Diagnosis of Large Industrial Equipment

Refurbishment and remanufacturing play a vital role in the sustainability of the large industrial field, which aims at restoring the equipment that is close to the end of their life. The EU-funded project RECLAIM proposes new approaches and techniques to support these two activities in order to achieve saving valuable materials and resources by renewing and recycling the mechanical equipment rather than scraping them when they exceed the end of the lifetime. As the most critical part of predictive maintenance in RECLAIM, the fault diagnosis technique could provide the necessary information about the identification of the failure type, thus making suitable maintenance strategies. In this paper, we propose a novel implementation method that can combine the digital twins with the fault diagnosis of large industrial equipment. Experiment result and analysis demonstrate that the proposed framework performs well for the fault diagnosis of rolling bearing

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Data-based fault detection in chemical processes: Managing records with operator intervention and uncertain labels

Developing data-driven fault detection systems for chemical plants requires managing uncertain data labels and dynamic attributes due to operator-process interactions. Mislabeled data is a known problem in computer science that has received scarce attention from the process systems community. This work introduces and examines the effects of operator actions in records and labels, and the consequences in the development of detection models. Using a state space model, this work proposes an iterative relabeling scheme for retraining classifiers that continuously refines dynamic attributes and labels. Three case studies are presented: a reactor as a motivating example, flooding in a simulated de-Butanizer column, as a complex case, and foaming in an absorber as an industrial challenge. For the first case, detection accuracy is shown to increase by 14% while operating costs are reduced by 20%. Moreover, regarding the de-Butanizer column, the performance of the proposed strategy is shown to be 10% higher than the filtering strategy. Promising results are finally reported in regard of efficient strategies to deal with the presented problemPeer ReviewedPostprint (author's final draft

A framework for effective management of condition based maintenance programs in the context of industrial development of E-Maintenance strategies

CBM (Condition Based Maintenance) solutions are increasingly present in industrial systems due to two main circumstances: rapid evolution, without precedents, in the capture and analysis of data and significant cost reduction of supporting technologies. CBM programs in industrial systems can become extremely complex, especially when considering the effective introduction of new capabilities provided by PHM (Prognostics and Health Management) and E-maintenance disciplines. In this scenario, any CBM solution involves the management of numerous technical aspects, that the maintenance manager needs to understand, in order to be implemented properly and effectively, according to the company’s strategy. This paper provides a comprehensive representation of the key components of a generic CBM solution, this is presented using a framework or supporting structure for an effective management of the CBM programs. The concept “symptom of failure”, its corresponding analysis techniques (introduced by ISO 13379-1 and linked with RCM/FMEA analysis), and other international standard for CBM open-software application development (for instance, ISO 13374 and OSA-CBM), are used in the paper for the development of the framework. An original template has been developed, adopting the formal structure of RCM analysis templates, to integrate the information of the PHM techniques used to capture the failure mode behaviour and to manage maintenance. Finally, a case study describes the framework using the referred template.Gobierno de Andalucía P11-TEP-7303 M