Big data acquisition and processing platform for embedded prognostics of the ultrasonic motor

This paper describes the architecture of a cross-sectorial Big Data platform for the process industry domain. The main objective was to design a scalable analytical platform that will support the collection, storage and processing of data from multiple industry domains. Such a platform should be able to connect to the existing environment in the plant and use the data gathered to build predictive functions to optimize the production processes. The analytical platform will contain a development environment with which to build these functions, and a simulation environment to evaluate the models. The platform will be shared among multiple sites from different industry sectors. Cross-sectorial sharing will enable the transfer of knowledge across different domains. During the development, we adopted a user-centered approach to gather requirements from different stakeholders which were used to design architectural models from different viewpoints, from contextual to deployment. The deployed architecture was tested in two process industry domains, one from the aluminium production and the other from the plastic molding industry.

This paper based on four key abilities of the Prognostics and Health Management (PHM) System including the abilities of fault detection, fault isolation, fault prognosis and prognosis of the remaining life, analyzes and constructs a framework of the PHM quantitative requirements, and defines the quantitative assessment emphasis of the PHM abilities. Targeting these quantitative requirements and proceeding from the viewpoint of life cycle assessment, this paper presents three methods of the PHM System validation, which are the validation method based on analysis and assessment, the validation method based on simulation (including entire simulation validation and half-entity simulation validation) and the validation method based on experimentation. This paper defines the emphasis on the PHM System analysis and assessment, ascertains the implementary elements and the simulated environmental framework of the PHM System simulated validation and elaborates two central elements of the PHM System experimental validation - the experiment scheme and the experiment technique. This paper provides thoughts and methods to the quantitative validation assessment of the rapidly developing and applying PHM System.

Prognostics and Health Management (PHM) is vital for complex engineering systems, yet its data-driven solutions are often hampered by the "small data problem"—a scarcity of labeled fault data in industrial settings. This limitation restricts the training and generalization of machine learning models and is compounded by varying operational conditions that reduce the relevance of historical data and pre-trained models. This research introduces a research framework to tackle these small data challenges in PHM. The primary objective is to develop a robust and adaptable PHM methodology by enhancing and synergistically integrating similarity-based Few-Shot Learning (FSL) with large-scale pre-trained time-series models. The research will focus on two main thrusts. First, it aims to improve the generalization capabilities of FSL frameworks by addressing limitations such as noise robustness, domain shift adaptability, and generalization to novel faults across diverse PHM domains. This involves developing noise-robust feature extraction, integrating domain adaptation techniques, and exploring expressive similarity metrics. Second, the study will investigate the effective adaptation of state-of-the-art pre-trained time-series models (e.g., TimesNet) for PHM tasks under data scarcity, focusing on efficient fine-tuning and synergistic integration with the enhanced FSL approaches. The author's prior success in a PHM data challenge using a similarity-based method for spacecraft systems provides preliminary validation. This research is expected to deliver an enhanced PHM framework for high-accuracy diagnostics with limited data, contributing generalized FSL models, systematic methods for leveraging pre-trained models in PHM, and advancing the practical deployment of intelligent PHM solutions.

Maintenance support evaluation models based on Prognostics and Health Management (PHM) are studied for the status quo of PHM application in new aviation equipment. At first, the qualitative influences of PHM two capabilities on maintenance support are analyzed, and the attributes and critical impacts of PHM are quantitatively modeled. Moreover, evaluation modeling is studied. Collecting availability as evaluation object parameter of maintenance support capability, evaluating methods are proposed. Overall framework of evaluation models is proposed integrated considering direct and indirect effect factors. And then maintenance support model flow with PHM is presented. Based on models constructed above, taking typical aviation equipment as an example the validity is verified. The study can support evaluating maintenance support capability based on PHM, at the same time, which can supply technological means for maintenance decision based on PHM making and optimizing.

Privacy protection for resource description framework data is very important because RDF (i.e., linked data) is widely used in published data format in many areas, including government open data, health-care for individuals, and social relationships. As data can include private information belonging to individuals or companies and can make private information available to third parties, there are several anonymization models provided for preserving privacy in practice. k-anonymity has thus gained attention in research. Recently, several RDF anonymization models have been proposed. However, current approaches focus on a model and a metric for measuring information loss but do not consider large-scale RDF data. In this paper, we propose an efficient anonymizing method for large-scale RDF data. We develop a greedy partitioning algorithm (i.e., SPARK) for RDF anonymization. SPARK is a leading platform for big data processing. The results of experiments on synthetic datasets demonstrate that our proposed method requires less running time than previous methods.

Prognostics and health management (PHM) represents a paradigm shift from legacy condition based maintenance (CBM) frameworks by expanding the potentials to accurately and robustly detect and diagnose incipient system faults. The ultimate goal of PHM is reliably predicting system failure times to allow for efficient maintenance scheduling either autonomously or by human decision makers (DM). In many industrial settings today the output from PHM systems constitutes a decision support system (DSS) used to aid DM, as entirely autonomous systems have not seen widespread industrial integration. However, there is relatively little support for engineers designing PHM systems in terms of human factors and how to provide the information in a way that actively supports human decision-making and this gap may result in limited use of PHM system by maintainers. The reliability of the information presented is a critical factor in the user acceptance and trust in a system. As a first step in developing such guidance, this paper reviews the implementation of other DSS and presents a design framework whereby PHM reliability levels are mapped against a suggested level of human input to the decision making process regarding required maintenance. The aim is to provide engineers with a guide to the level to which they should consider human factors and the presentation of information in the design of their PHM system. Fundamental to the suggested paradigm is that the uncertainties within a PHM system can be quantified, and as uncertainty increases, the requirement for DM to access additional information not explicitly tied to the PHM output increases. This information can form both explicit and tacit knowledge of a system or indeed industrial contexts surrounding decision implications, such as acceptable maintenance intervention windows in busy production schedules. As the complexity of a system or component being monitored is likely to affect the uncertainty within the PHM system associated with it, we are considering the overall cumulative uncertainty of a model output as the metric through which the required level of human input can be inferred. Coupled to this is the fact that increased model uncertainty is a causal factor in distrust and potential non-use of the model in industrial applications. It is the authors’ belief therefore that designing for increased human-model interaction concurrent with increasing model uncertainty may lead to a better engagement with PHM decision support capabilities, thereby offering the full advantages that PHM has to offer. The framework presented in this paper is an initial step towards facilitating the design of more usable and useful PHM systems.

Advanced control of large-scale wind turbines: Structural load reduction and lifetime control

The growing amount of data in healthcare industry has made inevitable the adoption of big data techniques in order to improve the quality of healthcare delivery. Despite the integration of big data processing approaches and platforms in existing data management architectures for healthcare systems, these architectures face difficulties in preventing emergency cases. The main contribution of this paper is proposing an extensible big data architecture based on both stream computing and batch computing in order to enhance further the reliability of healthcare systems by generating real-time alerts and making accurate predictions on patient health condition. Based on the proposed architecture, a prototype implementation has been built for healthcare systems in order to generate real-time alerts. The suggested prototype is based on spark and MongoDB tools.

This paper discusses an unsolicited proposal to NASA to implement the “Prognostics and Health Management (PHM) for Astronauts” project, the first international collaborative project to run on the International Space Station (ISS). The collaborative project is to validate the “PHM for Human Health and Performance (HH&P)” concept that the author introduced in his “System Health Management and Space Medicine Predictive Diagnostics. Common Concepts and Approaches.” paper in 2012. Since then the concept has been elaborated, refined, and further evolved in a dozen papers published by the author and his coauthors from the PHM Society and universities of United States and Canada. Following the NASA language for unsolicited proposals and discussing the project on validation of the advanced concept and corresponding technology candidates with predictive screening capability the paper articulates both the novelty of and need in implementing the to-be-validated concept on crew health maintenance in terms of recommendations by experienced astronauts and healthcare professionals on crewed space programs. The proposal particularly suggests NASA to employ PHM -based solutions with predictive screening capability, providing early and actionable real-time warnings of impending health issues that otherwise would have gone undetected unless a symptom is manifested or a sign is detected at a relatively late stage.

A review on prognostics and health management (PHM) methods of lithium-ion batteries

Prognostics and health management (PHM) is an enabling technology with the potential to solve complex reliability problems that have manifested due to complexity in design, manufacturing, test, and maintenance. This chapter provides an assessment of the state of practice and state of the art in PHM, focused mostly on electronics, and identify the key research and development (R&D) opportunities and challenges that exist, so that resources can be efficiently allocated. In assessing the state of the art and trends for the development of a roadmap for PHM, differences between PHM for electronics and PHM for mechanical structures must be recognized. The foundations of electronic systems are the integrated circuits (ICs) that comprise the computing, processing, memory, and communications. Photo‐electronic components that could greatly benefit from PHM include light‐emitting diodes (LEDs), lasers, radar, infra‐red devices, and tactical sensors. Health monitoring and identifying a baseline usage condition to evaluate system health are fundamental for prognostics.

Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications

Individuals who work in the field of prognostic and health management (PHM) technology have come to understand that PHM can provide the ability to effectively manage the operation, maintenance and logistic support of individual assets or groups of assets through the availability of regularly updated and detailed health information. Naturally, prospective customers of PHM technology ask, 'How will the implementation of PHM benefit my organization?' Typically, the response by individuals in the field is, 'Anecdotal evidence indicates that PHM decreases maintenance costs, increases operational availability and improves safety'. This information helps the prospective customer understand the practical benefits of the technology but that customer stills needs more information to justify their investment in the technology. The customer needs a calculated return on investment (ROI) figure for their particular asset that provides financial assessment of the benefit of the investment. The data, time and expertise required to conduct a rigorous cost benefit analysis makes the effort seem daunting to the average engineer with little to no financial analysis training. The reality is that with a cursory understanding of the asset operation, maintenance and logistic issues, a useful cost benefit analysis can be conducted by engineers without business school training. The purpose of this paper is to provide a general methodology for conducting a preliminary cost benefit analysis that calculates an ROI for PHM implementation. The paper will discuss the general types of information needed for the analysis, the quantifying of expected benefits and the types of supporting data required to validate the benefit assumptions as well as an outline for the costing of the PHM technology.

Prognostics and Health Management (PHM) is a crucial aspect of industrial engineering, aiming to enhance equipment reliability, reduce downtime, and optimize maintenance strategies. Advanced data analytics plays a pivotal role in PHM, enabling the analysis of equipment condition and performance to predict failures and prescribe proactive maintenance actions. This review article provides a comprehensive overview of advanced data analytics techniques used in PHM, including machine learning, statistical analysis, and predictive modeling. The article also discusses the application of these techniques in various industrial sectors, highlighting their benefits and challenges. Furthermore, the review identifies current trends and future directions in the field of PHM, emphasizing the importance of integrating advanced data analytics for improved industrial engineering practices.

Advances in the manufacturing industry have led to modern approaches such as Industry 4.0, Cyber-Physical Systems, Smart Manufacturing (SM) and Digital Twins. The traditional manufacturing architecture that consisted of hierarchical layers has evolved into a hierarchy-free network in which all the areas of a manufacturing enterprise are interconnected. The field devices on the shop floor generate large amounts of data that can be useful for maintenance planning. Prognostics and Health Management (PHM) approaches use this data and help us in fault detection and Remaining Useful Life (RUL) estimation. Although there is a significant amount of research primarily focused on tool wear prediction and Condition-Based Monitoring (CBM), there is not much importance given to the multiple facets of PHM. This paper conducts a review of PHM approaches, the current research trends and proposes a three-phased interoperable framework to implement Smart Prognostics and Health Management (SPHM). The uniqueness of SPHM lies in its framework, which makes it applicable to any manufacturing operation across the industry. The framework consists of three phases: Phase 1 consists of the shopfloor setup and data acquisition steps, Phase 2 describes steps to prepare and analyze the data and Phase 3 consists of modeling, predictions and deployment. The first two phases of SPHM are addressed in detail and an overview is provided for the third phase, which is a part of ongoing research. As a use-case, the first two phases of the SPHM framework are applied to data from a milling machine operation.