The objective of this paper is to present a comprehensive review of the contemporary techniques for fault detection, diagnosis, and prognosis of rolling element bearings (REBs). Data-driven approaches, as opposed to model-based approaches, are gaining in popularity due to the availability of low-cost sensors and big data. This paper first reviews the fundamentals of prognostics and health management (PHM) techniques for REBs. A brief description of the different bearing-failure modes is given, then, the paper presents a comprehensive representation of the different health features (indexes, criteria) used for REB fault diagnostics and prognostics. Thus, the paper provides an overall platform for researchers, system engineers, and experts to select and adopt the best fit for their applications. Second, the paper provides overviews of contemporary REB PHM techniques with a specific focus on modern artificial intelligence (AI) techniques (i.e., shallow learning algorithms). Finally, deep-learning approaches for fault detection, diagnosis, and prognosis for REB are comprehensively reviewed.

A comprehensive review of artificial intelligence-based approaches for rolling element bearing PHM: shallow and deep learning

Developing an online data-driven approach for prognostics and health management of lithium-ion batteries

A novel model with the ability of few-shot learning and quick updating for intelligent fault diagnosis

Envisioning maintenance 5.0: Insights from a systematic literature review of Industry 4.0 and a proposed framework

Smart manufacturing in intelligent digital mesh: Integration of enterprise architecture and software product line engineering

Prognostics and health management (PHM) has emerged as an intelligent solution to improve the availability of manufacturing systems. PHM consists of system health monitoring, feature extraction, fault diagnosis, and fault prognosis through remaining useful life estimation. However, the application of PHM to manufacturing systems is challenging because systems have become more complex and uncertain. In particular, small and medium-sized enterprises have difficulty in applying PHM due to the lack of internal expertise, time and resources for research and development. The objective of this paper is to develop a framework to provide a readily usable and accessible guideline for PHM application to manufacturing systems. A survey was performed to gather the current practices in dealing with system failures and maintenance strategies in the field. A framework was developed for giving a guideline for PHM application based on common core modules across manufacturing systems and their kinds with respect to the amount of available data and domain knowledge. A reference table was developed to track the PHM techniques for feature extraction, fault diagnosis, and fault prognosis. Finally, fault prognosis of a system was conducted as a case study, following the framework and the reference table to verify its practical use.

A Framework for Prognostics and Health Management Applications toward Smart Manufacturing Systems

Electronics are subjected to thermal, mechanical or chemical stress conditions during their operations. These stress conditions accumulate damages to the electronic system, adversely affecting its components and interconnects including solder joints. Solder joint degradation can cause system malfunctioning, which eventually results in catastrophic failures. In order to prevent system failures, it is required to manage the system health continuously by monitoring damages such as solder joint degradation. Regardless of stress conditions, solder joints often start to degrade from their surface where high speed signals in an electric circuit are concentrated due to the phenomenon referred to as the skin effect. Thus, high speed signals such as digital signals can serve as a sensitive means of detecting solder joint degradation at early stages. This paper presents a diagnosing method of assessing solder joint reliability based on digital signal characterization. Accelerated life tests are conducted to generate solder joint failure. The test setup consists of a circuit board with solder joints, a digital signal transceiver, an environmental test chamber and a stress application fixture. Constant mechanical shear stress is applied to the solder joints of a circuit board at an elevated temperature. A digital signal transceiver generates high speed signals travelling through the solder joints, and continuously monitors the signal characteristics which indicate signal integrity. The test results confirm that the signal characteristics show statistically significant variations between intact and cracked state of the solder joint, and the parameter variations indicate the deterioration of the signal integrity. These results suggest that digital communication signals can be used as a non-destructive diagnostic tool of physical degradation. It can be improved as a tool which provides early warning of impending system failures without installing additional fault sensing instruments. This diagnostic approach may serve as a proactive prognostic module that allows for real-time health management of electronic products and systems.

A prognostic method of assessing solder joint reliability based on digital signal characterization

Qualification includes all activities to demonstrate that a product meets and exceeds the reliability goals Manufacturers need to spend time and resources for the qualification processes under the pressure of reducing time to market, as well as offering a competitive price Failure to qualify a product could result in economic loss such as warranty and recall claims and the manufacturer could lose the reputation in the market In order to provide valid and reliable qualification results, manufacturers are required to make extra effort based on the operational and environmental characteristics of the product This paper discusses optimal interval censoring design for reliability prediction of electronic packages under limited time and resources This design should provide more accurate assessment of package capability and thus deliver better reliability prediction

/pdf/optimal-interval-censoring-design-for-reliability-prediction-1mc8lx024m.pdf

Optimal Interval Censoring Design for Reliability Prediction of Electronic Packages

Electronic products and systems are widely used in industrial network systems, control devices, and data acquisition devices across many industry sectors. Failures of such electronic systems might lead to unexpected downtime, loss of productivity, additional work for repairs, and delay in product and service development. Thus, developing an appropriate sensing technique is necessary, because it is the first step in system fault diagnosis and prognosis. Many sensing techniques often require external and additional sensing devices, which might disturb system operation and consequently increase operating costs. In this study, we present an on-chip health sensing method for non-destructive and non-invasive interconnect degradation detection. Bit error rate (BER), which represents data integrity during digital signal transmission, was selected to sense interconnect health without connecting external sensing devices. To verify the health sensing performance, corrosion tests were conducted with in situ monitoring of the BER and direct current (DC) resistance. The eye size, extracted from the BER measurement, showed the highest separation between the intact and failed interconnect, as well as a gradual transition, compared with abrupt changes in the DC resistance, during interconnect degradation. These experimental results demonstrate the potential of the proposed sensing method for on-chip interconnect health monitoring applications without disturbing system operation.

/pdf/development-of-a-non-invasive-on-chip-interconnect-health-4lt6tv0wjp.pdf

Development of a Non-Invasive On-Chip Interconnect Health Sensing Method Based on Bit Error Rates.

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Papers

A Framework for Prognostics and Health Management Applications toward Smart Manufacturing Systems

A prognostic method of assessing solder joint reliability based on digital signal characterization

Optimal Interval Censoring Design for Reliability Prediction of Electronic Packages

Development of a Non-Invasive On-Chip Interconnect Health Sensing Method Based on Bit Error Rates.