Post-prognostics decision making in distributed MEMS-based systems

In this paper, the problem of using prognostics information of Micro-Electro-Mechanical Systems (MEMS) for post-prognostics decision in distributed MEMS-based systems is addressed. A strategy of postprognostics decision is proposed and then implemented in a distributed MEMS-based conveying surface. The surface is designed to convey fragile and tiny microobjects. The purpose is to use the prognostics results of the used MEMS in the form of Remaining Useful Life (RUL) to maintain as long as possible a good performance of the conveying surface. For that, a distributed algorithm for distributed decision making in dynamic conditions is proposed. In addition, a simulator to simulate the decision in the targeted system is developed. Simulation results show the importance of the postprognostics decision to optimize the utilization of the system and improve its performance

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security

A hybrid prognostics approach for MEMS: From real measurements to remaining useful life estimation

This paper presents a hybrid prognostics approach for Micro Electro-Mechanical Systems (MEMS). This approach relies on two phases: an offline phase for the MEMS and its degradation modeling, and an online phase where the obtained degradation model is used with the available data for prognostics. In the online phase, the particle filter algorithm is used to perform online parameters estimation of the degradation model and predict the Remaining Useful Life (RUL) of MEMS. The effectiveness of the proposed approach is validated on experimental data related to an electro-thermally actuated MEMS valv

Prognostics and health management for maintenance practitioners - Review, implementation and tools evaluation.

In literature, prognostics and health management (PHM) systems have been studied by many researchers from many different engineering fields to increase system reliability, availability, safety and to reduce the maintenance cost of engineering assets. Many works conducted in PHM research concentrate on designing robust and accurate models to assess the health state of components for particular applications to support decision making. Models which involve mathematical interpretations, assumptions and approximations make PHM hard to understand and implement in real world applications, especially by maintenance practitioners in industry. Prior knowledge to implement PHM in complex systems is crucial to building highly reliable systems. To fill this gap and motivate industry practitioners, this paper attempts to provide a comprehensive review on PHM domain and discusses important issues on uncertainty quantification, implementation aspects next to prognostics feature and tool evaluation. In this paper, PHM implementation steps consists of; (1) critical component analysis, (2) appropriate sensor selection for condition monitoring (CM), (3) prognostics feature evaluation under data analysis and (4) prognostics methodology and tool evaluation matrices derived from PHM literature. Besides PHM implementation aspects, this paper also reviews previous and on-going research in high-speed train bogies to highlight problems faced in train industry and emphasize the significance of PHM for further investigations

Fault prognostics of micro-electro-mechanical systems using particle filtering

This paper presents a hybrid prognostics approach for Micro-Electro-Mechanical Systems (MEMS). The approach relies on two phases: an offline phase for the MEMS and its degradation modeling, and an online phase for its fault prognostics. The proposed approach is applied to a MEMS device consisting in an electro-thermally actuated valve. In the offline phase, an experimental platform is built to validate the obtained nominal behavior model of the targeted MEMS and to get its degradation model. This model represents the drifts in a MEMS physical parameter, which is its compliance. In the online phase, a particle filter algorithm is used to perform online parameters estimation of the derived degradation model and calculate the MEMS remaining useful life. The obtained prognostic results show the effectiveness of the proposed approach

Big Data and the Internet of Things

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.Comment: 33 pages. draft of upcoming book chapter in Japkowicz and Stefanowski (eds.) Big Data Analysis: New algorithms for a new society, Springer Series on Studies in Big Data, to appea

A Prescriptive Maintenance Aligned Production Planning and Control Reference Process

Digital innovations can improve various business processes, such as production planning and control (PPC). In the last years, prescriptive maintenance (PxM) emerged as a strategy to increase overall production performance, but an alignment of the PPC process with PxM has not been examined yet. To tackle this problem, a PxM-aligned PPC process is designed and evaluated in this study using a reference model development methodology, including a narrative literature review, a multivocal literature review, and eight expert interviews. The reference model shows where process elements benefit from PxM alignment, how alignment can be achieved from a process and output, data, function, and organization view, and where fits and gaps between theory and practice are

Enhancing Decisions in Prognostics and Health Management Framework

Prognostics and health management have become increasingly important in recent years. Many research studies focus on a crucial phase consisting of predicting the remaining useful life of equipment or a component. However, this step is often carried out without taking into account the decisions that will be taken later. This article aims to propose a modification of the existing PHM framework to combine the prognostics and decision-making phases in a closed loop. In this paper, the presented framework is described and some elements for its implementation are proposed. A simplified example is developed to illustrate the presented methodology of post-prognostic decision enhancement

MICROELECTRONICS PACKAGING TECHNOLOGY ROADMAPS, ASSEMBLY RELIABILITY, AND PROGNOSTICS

This paper reviews the industry roadmaps on commercial-off-the shelf (COTS) microelectronics packaging technologies covering the current trends toward further reducing size and increasing functionality. Due tothe breadth of work being performed in this field, this paper presents only a number of key packaging technologies. The topics for each category were down-selected by reviewing reports of industry roadmaps including the International Technology Roadmap for Semiconductor (ITRS) and by surveying publications of the International Electronics Manufacturing Initiative (iNEMI) and the roadmap of association connecting electronics industry (IPC). The paper also summarizes the findings of numerous articles and websites that allotted to the emerging and trends in microelectronics packaging technologies. A brief discussion was presented on packaging hierarchy from die to package and to system levels. Key elements of reliability for packaging assemblies were presented followed by reliabilty definition from a probablistic failure perspective. An example was present for showing conventional reliability approach using Monte Carlo simulation results for a number of plastic ball grid array (PBGA). The simulation results were compared to experimental thermal cycle test data. Prognostic health monitoring (PHM) methods, a growing field for microelectronics packaging technologies, were briefly discussed. The artificial neural network (ANN), a data-driven PHM, was discussed in details. Finally, it presented inter- and extra-polations using ANN simulation for thermal cycle test data of PBGA and ceramic BGA (CBGA) assemblies