Residual life prediction and degradation-based control of multi-component systems

The condition monitoring of multi-component systems utilizes multiple sensors to capture the functional condition of the systems and allows the sensor information to be used to reason about the health information of the systems or components. Chapter 3 considers the situation when sensor signals capture unknown mixtures of component signals and proposes a two-stage vibration-based methodology to identify component degradation signals from mixed sensor signals in order to predict component-level residual lives. Specifically, we are interested in modeling the degradation of systems that consist of two or more identical components operating under similar conditions. Chapter 4 focuses on the interactive relationship between tool wear (component degradation) and product quality degradation (sensor information) that widely exists in multistage manufacturing processes and proposes a high-dimensional stochastic differential equation model to capture the interaction relationship. Then, real-time quality measurements are incorporated to online predict the residual life of the system. Chapter 5 develops a strategy of dynamic workload adjustment for parallel multi-component systems in order to control the degradation processes and failure times of individual components, for the purpose of preventing the overlap of component failures. This chapter opens a new research direction that focuses on the active control of degradation rather than only the modeling part.Ph.D

Investigation of degradation and upgradation models for flexible unit systems: a systematic literature review

Research on flexible unit systems (FUS) with the context of descriptive, predictive, and prescriptive analysis have remarkably progressed in recent times, being now reinforced in the current Industry 4.0 era with the increased focus on integration of distributed and digitalized systems. In the existing literature, most of the work focused on the individual contributions of the above mentioned three analyses. Moreover, the current literature is unclear with respect to the integration of degradation and upgradation models for FUS. In this paper, a systematic literature review on degradation, residual life distribution, workload adjustment strategy, upgradation, and predictive maintenance as major performance measures to investigate the performance of the FUS has been considered. In order to identify the key issues and research gaps in the existing literature, the 59 most relevant papers from 2009 to 2020 have been sorted and analyzed. Finally, we identify promising research opportunities that could expand the scope and depth of FUS.The project is funded by the Department of Science and Technology, Science & Engineering Research Board (DST-SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808, and also by FCT—Fundação para a Ciência e Tecnologia through the R&D Units Project Scope: UIDB/00319/2020

Collaborative Networks, Decision Systems, Web Applications and Services for Supporting Engineering and Production Management

This book focused on fundamental and applied research on collaborative and intelligent networks and decision systems and services for supporting engineering and production management, along with other kinds of problems and services. The development and application of innovative collaborative approaches and systems are of primer importance currently, in Industry 4.0. Special attention is given to flexible and cyber-physical systems, and advanced design, manufacturing and management, based on artificial intelligence approaches and practices, among others, including social systems and services

Casing structural integrity and failure modes in a range of well types: a review.

This paper focus on factors attributing to casing failure, their failure mechanism and the resulting failure mode. The casing is a critical component in a well and the main mechanical structural barrier element that provide conduits and avenue for oil and gas production over the well lifecycle and beyond. The casings are normally subjected to material degradation, varying local loads, induced stresses during stimulation, natural fractures, slip and shear during their installation and operation leading to different kinds of casing failure modes. The review paper also covers recent developments in casing integrity assessment techniques and their respective limitations. The taxonomy of the major causes and cases of casing failure in different well types is covered. In addition, an overview of casing trend utilisation and failure mix by grades is provided. The trend of casing utilisation in different wells examined show deep-water and shale gas horizontal wells employing higher tensile grades (P110 & Q125) due to their characteristics. Additionally, this review presents casing failure mixed by grades, with P110 recording the highest failure cases owing to its stiffness, high application in injection wells, shale gas, deep-water and high temperature and high temperature (HPHT) wells with high failure probability. A summary of existing tools used for the assessment of well integrity issues and their respective limitations is provided and conclusions drawn

Research and Technology, 1994

This report selectively summarizes the NASA Lewis Research Center's research and technology accomplishments for the fiscal year 1994. It comprises approximately 200 short articles submitted by the staff members of the technical directorates. The report is organized into six major sections: Aeronautics, Aerospace Technology, Space Flight Systems, Engineering and Computational Support, Lewis Research Academy, and Technology Transfer. A table of contents and author index have been developed to assist the reader in finding articles of special interest. This report is not intended to be a comprehensive summary of all research and technology work done over the past fiscal year. Most of the work is reported in Lewis-published technical reports, journal articles, and presentations prepared by Lewis staff members and contractors. In addition, university grants have enabled faculty members and graduate students to engage in sponsored research that is reported at technical meetings or in journal articles. For each article in this report a Lewis contact person has been identified, and where possible, reference documents are listed so that additional information can be easily obtained. The diversity of topics attests to the breadth of research and technology being pursued and to the skill mix of the staff that makes it possible

NASA SBIR abstracts of 1991 phase 1 projects

The objectives of 301 projects placed under contract by the Small Business Innovation Research (SBIR) program of the National Aeronautics and Space Administration (NASA) are described. These projects were selected competitively from among proposals submitted to NASA in response to the 1991 SBIR Program Solicitation. The basic document consists of edited, non-proprietary abstracts of the winning proposals submitted by small businesses. The abstracts are presented under the 15 technical topics within which Phase 1 proposals were solicited. Each project was assigned a sequential identifying number from 001 to 301, in order of its appearance in the body of the report. Appendixes to provide additional information about the SBIR program and permit cross-reference of the 1991 Phase 1 projects by company name, location by state, principal investigator, NASA Field Center responsible for management of each project, and NASA contract number are included

Prognostic-based Life Extension Methodology with Application to Power Generation Systems

Practicable life extension of engineering systems would be a remarkable application of prognostics. This research proposes a framework for prognostic-base life extension. This research investigates the use of prognostic data to mobilize the potential residual life. The obstacles in performing life extension include: lack of knowledge, lack of tools, lack of data, and lack of time. This research primarily considers using the acoustic emission (AE) technology for quick-response diagnostic. To be specific, an important feature of AE data was statistically modeled to provide quick, robust and intuitive diagnostic capability. The proposed model was successful to detect the out of control situation when the data of faulty bearing was applied. This research also highlights the importance of self-healing materials. One main component of the proposed life extension framework is the trend analysis module. This module analyzes the pattern of the time-ordered degradation measures. The trend analysis is helpful not only for early fault detection but also to track the improvement in the degradation rate. This research considered trend analysis methods for the prognostic parameters, degradation waveform and multivariate data. In this respect, graphical methods was found appropriate for trend detection of signal features. Hilbert Huang Transform was applied to analyze the trends in waveforms. For multivariate data, it was realized that PCA is able to indicate the trends in the data if accompanied by proper data processing. In addition, two algorithms are introduced to address non-monotonic trends. It seems, both algorithms have the potential to treat the non-monotonicity in degradation data. Although considerable research has been devoted to developing prognostics algorithms, rather less attention has been paid to post-prognostic issues such as maintenance decision making. A multi-objective optimization model is presented for a power generation unit. This model proves the ability of prognostic models to balance between power generation and life extension. In this research, the confronting objective functions were defined as maximizing profit and maximizing service life. The decision variables include the shaft speed and duration of maintenance actions. The results of the optimization models showed clearly that maximizing the service life requires lower shaft speed and longer maintenance time

Explainable Predictive Maintenance

Explainable Artificial Intelligence (XAI) fills the role of a critical interface fostering interactions between sophisticated intelligent systems and diverse individuals, including data scientists, domain experts, end-users, and more. It aids in deciphering the intricate internal mechanisms of ``black box'' Machine Learning (ML), rendering the reasons behind their decisions more understandable. However, current research in XAI primarily focuses on two aspects; ways to facilitate user trust, or to debug and refine the ML model. The majority of it falls short of recognising the diverse types of explanations needed in broader contexts, as different users and varied application areas necessitate solutions tailored to their specific needs. One such domain is Predictive Maintenance (PdM), an exploding area of research under the Industry 4.0 \& 5.0 umbrella. This position paper highlights the gap between existing XAI methodologies and the specific requirements for explanations within industrial applications, particularly the Predictive Maintenance field. Despite explainability's crucial role, this subject remains a relatively under-explored area, making this paper a pioneering attempt to bring relevant challenges to the research community's attention. We provide an overview of predictive maintenance tasks and accentuate the need and varying purposes for corresponding explanations. We then list and describe XAI techniques commonly employed in the literature, discussing their suitability for PdM tasks. Finally, to make the ideas and claims more concrete, we demonstrate XAI applied in four specific industrial use cases: commercial vehicles, metro trains, steel plants, and wind farms, spotlighting areas requiring further research.Comment: 51 pages, 9 figure