Extreme Learning Machine Based Prognostics of Battery Life

This paper presents a prognostic scheme for estimating the remaining useful life of Lithium-ion batteries. The proposed scheme utilizes a prediction module that aims to obtain precise predictions for both short and long prediction horizons. The prediction module makes use of extreme learning machines for one-step and multi-step ahead predictions, using various prediction strategies, including iterative, direct and DirRec, which use the constant-current experimental capacity data for the estimation of the remaining useful life. The data-driven prognostic approach is highly dependent on the availability of high quantity of quality observations. Insufficient amount of available data can result in unsatisfactory prognostics. In this paper, the prognostics scheme is utilized to estimate the remaining useful life of a battery, with insufficient direct data available, but taking advantage of observations available from a fleet of similar batteries with similar working conditions. Experimental results show that the proposed prognostic scheme provides a fast and efficient estimation of the remaining useful life of the batteries and achieves superior results when compared with various state-of-the-art prediction techniques

Failure Prognosis of Wind Turbine Components

Wind energy is playing an increasingly significant role in the World\u27s energy supply mix. In North America, many utility-scale wind turbines are approaching, or are beyond the half-way point of their originally anticipated lifespan. Accurate estimation of the times to failure of major turbine components can provide wind farm owners insight into how to optimize the life and value of their farm assets. This dissertation deals with fault detection and failure prognosis of critical wind turbine sub-assemblies, including generators, blades, and bearings based on data-driven approaches. The main aim of the data-driven methods is to utilize measurement data from the system and forecast the Remaining Useful Life (RUL) of faulty components accurately and efficiently. The main contributions of this dissertation are in the application of ALTA lifetime analysis to help illustrate a possible relationship between varying loads and generators reliability, a wavelet-based Probability Density Function (PDF) to effectively detecting incipient wind turbine blade failure, an adaptive Bayesian algorithm for modeling the uncertainty inherent in the bearings RUL prediction horizon, and a Hidden Markov Model (HMM) for characterizing the bearing damage progression based on varying operating states to mimic a real condition in which wind turbines operate and to recognize that the damage progression is a function of the stress applied to each component using data from historical failures across three different Canadian wind farms

Lithium-ion battery remaining useful life prediction

Lithium-Ion Battery Remaining Useful Life Prediction work consists of an initial approach to battery life prediction using Severson dataset and data-driven LSTM-based RUL predictive methods. Although the memory is written in English, the presentation will be presented in Spanish

A Review: Prognostics and Health Management in Automotive and Aerospace

Prognostics and Health Management (PHM) attracts increasing interest of many researchers due to its potentially important applications in diverse disciplines and industries. In general, PHM systems use real-time and historical state information of subsystems and components of the operating systems to provide actionable information, enabling intelligent decision-making for improved performance, safety, reliability, and maintainability. Every year, a substantial number of papers in this area including theory and practical applications, appear in academic journals, conference proceedings and technical reports. This paper aims to summarize and review researches, developments and recent contributions in PHM for automotive- and aerospace industries. It can also be considered as the starting point for researchers and practitioners in general to assist them through PHM implementation and help them to accomplish their work more easily.Algorithms and the Foundations of Software technolog

Contribution to intelligent monitoring and failure prognostics of industrial systems.

This thesis was conducted within the framework of SMART project funded by a European program, Interreg POCTEFA. The project aims to support small and medium-sized companies to increase their competitiveness in the context of Industry 4.0 by developing intelligent monitoring tools for autonomous system health management. To do so, in this work, we propose efficient data-driven algorithms for prognostics and health management of industrial systems. The first contribution consists of the construction of a new robust health indicator that allows clearly separating different fault states of a wide range of systems’ critical components. This health indicator is also efficient when considering multiples monitoring parameters under various operating conditions. Next, the second contribution addresses the challenges posed by online diagnostics of unknown fault types in dynamic systems, particularly the detection, localization, and identification of the robot axes drifts origin when these drifts have not been learned before. For this purpose, a new online diagnostics methodology based on information fusion from direct and indirect monitoring techniques is proposed. It uses the direct monitoring way to instantaneously update the indirect monitoring model and diagnose online the origin of new faults. Finally, the last contribution deals with the prognostics issue of systems failure in a controlled industrial process that can lead to negative impacts in long-term predictions. To remedy this problem, we developed a new adaptive prognostics approach based on the combination of multiple machine learning predictions in different time horizons. The proposed approach allows capturing the degradation trend in long-term while considering the state changes in short-term caused by the controller activities, which allows improving the accuracy of prognostics results. The performances of the approaches proposed in this thesis were investigated on different real case studies representing the demonstrators of the thesis partners

Novel deep cross-domain framework for fault diagnosis or rotary machinery in prognostics and health management

Improving the reliability of engineered systems is a crucial problem in many applications in various engineering fields, such as aerospace, nuclear energy, and water declination industries. This requires efficient and effective system health monitoring methods, including processing and analyzing massive machinery data to detect anomalies and performing diagnosis and prognosis. In recent years, deep learning has been a fast-growing field and has shown promising results for Prognostics and Health Management (PHM) in interpreting condition monitoring signals such as vibration, acoustic emission, and pressure due to its capacity to mine complex representations from raw data. This doctoral research provides a systematic review of state-of-the-art deep learning-based PHM frameworks, an empirical analysis on bearing fault diagnosis benchmarks, and a novel multi-source domain adaptation framework. It emphasizes the most recent trends within the field and presents the benefits and potentials of state-of-the-art deep neural networks for system health management. Besides, the limitations and challenges of the existing technologies are discussed, which leads to opportunities for future research. The empirical study of the benchmarks highlights the evaluation results of the existing models on bearing fault diagnosis benchmark datasets in terms of various performance metrics such as accuracy and training time. The result of the study is very important for comparing or testing new models. A novel multi-source domain adaptation framework for fault diagnosis of rotary machinery is also proposed, which aligns the domains in both feature-level and task-level. The proposed framework transfers the knowledge from multiple labeled source domains into a single unlabeled target domain by reducing the feature distribution discrepancy between the target domain and each source domain. Besides, the model can be easily reduced to a single-source domain adaptation problem. Also, the model can be readily updated to unsupervised domain adaptation problems in other fields such as image classification and image segmentation. Further, the proposed model is modified with a novel conditional weighting mechanism that aligns the class-conditional probability of the domains and reduces the effect of irrelevant source domain which is a critical issue in multi-source domain adaptation algorithms. The experimental verification results show the superiority of the proposed framework over state-of-the-art multi-source domain-adaptation models

Model-Form Uncertainty Quantification in Prognosis and Fleet Management with Physics-Informed Neural Networks

Prognosis and health management play an important role in the control of costs associated with the operation of large industrial equipment. By properly comprehending hardware degradation and accurately predicting the remaining useful life of such equipment, we can significantly lower operational costs by reducing asset downtime and maintenance interventions. However, complex interactions between operational conditions and component capability make accurately modeling damage accumulation for large fleets a daunting task. Unforeseen factors such as aggressive missions introduced by operators, exposure to harsh environments, manufacturing issues, amongst many others, can lead to large discrepancies between predicted and observed useful life. Motivated by the growing availability of data and computational power as well as the advances in hybrid modeling frameworks, capable of merging elements of physics, machine learning, and statistical learning, in this dissertation, we focus on the development of novel approaches to minimize the impact of unforeseen factors in fleet management. In this dissertation, we focus on the challenges of accounting for the impacts of such unforeseen factors on two specific stages of a component service life; early-file and end-life. Two numerical case studies are derived to emulate two common issues in fleet life management; manufacturing issues leading to an infant mortality problem, and unexpected exposure to harsher environments by operators, accelerating wear-out and significantly reducing component\u27s useful life. In the first analysis, two key aspects in a prognosis and health management perspective are addressed; detecting the emerging issue (i.e., the infant mortality problem), and the evaluation of risk mitigation procedures to minimize/mitigate its effects on the overall fleet reliability. Bayesian networks implementing physics-based models are used to model the fleet unreliability and assist in the quantification of the infant mortality impact on the fleet useful life. Additionally, steps to adapted the derived Bayesian networks to assist in the evaluation of possible mitigation approaches to minimize the impacts of fleet-wide early life problems are presented. Concerning the wear-out analysis, a civil aviation case study is derived, in which an aircraft fleet mainly operates in coastal routes, significantly increasing its exposure to saline corrosion. These conditions lead to accelerated degradation of the aircraft wing panels due to the combined effects of corrosion and mechanical fatigue. Such corrosive conditions are not accounted for by the fleet prognosis model generating a significant epistemic uncertainty (i.e., a missing physics issue). To address this issue, we proposed hybrid recurrent neural network modules to compensate for the model-form uncertainty. In the formulated neural network cell, well-understood aspects of the degradation mechanism are addressed by a physics-based model, while data-driven models are trained to account for the missing physics effects. After proper training, the hybrid neural network can compensate for the unaccounted effects in the model damage forecast and generates accurate predictions to assist in the fleet prognosis analysis. Obtained results illustrate the capabilities of the proposed frameworks in compensating for the considered unforeseen factors impacts in fleet management. Additionally, the obtained results have prominently shown the significance and importance of properly account for such factors on fleet prognosis and how these factors can drastically hinder engineers\u27 ability to properly perform prognosis and health management analysis

To Tolerate or To Impute Missing Values in V2X Communications Data?

Misbehavior detection is a critical task in vehicular ad hoc networks. However, state-of-the-art data-driven techniques for misbehavior detection are usually conducted through complete V2X communication data collected from simulated experiments. This thesis evaluates the main strategies for the treatment of missing values to find out the best match for misbehavior detection with incomplete V2X communication data. This thesis proposes three novel methods for imputing and tolerating missing data. The first two are novel imputation methods that are based on cooperative clustering and collaborative clustering. The latter is a missing-tolerant method that is an ensemble learning based on the random subspace selection and Dempster-Shafer fusion. The effectiveness of the proposed techniques is evaluated in the ground truth vehicular reference misbehavior data. Moreover, a multi-factor amputation framework has been developed to induce missingness over V2X communication data with different missing ratios, mechanisms, and distributions. This framework provides a comprehensive benchmark resembling missingness over V2X communication data. The proposed methods are compared with some missing-tolerant and imputation methods. The attained results over benchmark data are analyzed and indicated the winner treatments in each aspect

Forecasting in Mathematics

Mathematical probability and statistics are an attractive, thriving, and respectable part of mathematics. Some mathematicians and philosophers of science say they are the gateway to mathematics’ deepest mysteries. Moreover, mathematical statistics denotes an accumulation of mathematical discussions connected with efforts to most efficiently collect and use numerical data subject to random or deterministic variations. Currently, the concept of probability and mathematical statistics has become one of the fundamental notions of modern science and the philosophy of nature. This book is an illustration of the use of mathematics to solve specific problems in engineering, statistics, and science in general