Machine Fault Diagnosis and Prognosis: The State of The Art

Abstract

Machine fault diagnostic and prognostic techniques have been the considerable subjects of condition-based maintenance system in the recent time due to the potential advantages that could be gained from reducing downtime, decreasing maintenance costs, and increasing machine availability. For the past few years, research on machine fault diagnosis and prognosis has been developing rapidly. These publications covered in the wide range of statistical approaches to model-based approaches. With the aim of synthesizing and providing the information of these researches for researcher's community, this paper attempts to summarize and classify the recent published techniques in diagnosis and prognosis of rotating machinery. Furthermore, it also discusses the opportunities as well as the challenges for conducting advance research in the field of machine prognosis.

WITHDRAWN: IoT-based Machinery Failure Predictive Solution using Big Data Analysis on the MIMII Dataset

“…We are proposing an IoT-based machinery failure predictive solution as a CBM predictive maintenance technique. Hence, our solution consists of the three phases [1]: data acquisition, data processing and decision making. In a prior work [11], we presented the system architecture of our solution.…”

“…Thus, we chose to complete each vector by 102144 dummy data to reach a window size equal to 2 18 . Each vector is then represented by (1). By adding dummy data, we obtained vector data covering 10-seconds length valve operation and of a window size equal to 2 18 .…”

“…Condition-Based Monitoring (CBM) is considered as one of the most commonly used predictive maintenance solutions. This approach relies on the monitoring of the machine and is based on three key components [1]: data acquisition, data processing and decision making. With the progress we are witnessing today in sensor modules, wireless communication techniques, data storage solutions and Big Data Analysis methods, IoTbased solutions are massively developed.…”

Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review

“…Therefore, the maintenance of machinery plays a vital role in industries and has been studied by researchers for decades. There are three common maintenance strategies arranged in order of progression: breakdown maintenance, preventive maintenance and predictive maintenance [187]. Breakdown maintenance is an unplanned maintenance way to maintain the damaged machines through the component repair or direct replacement, which seriously affects the continuity of industrial production and brings dramatically high maintenance costs.…”

A switching ensemble approach for remaining useful life estimation of electrolytic capacitors

“…Despite the fact that these approaches lead to accurate prognostics results, uncertainty arising due to the assumptions and simplifications of the adopted models may pose limitations on their practical deployment (Chiang et al 2001;Di Maio et al 2013). On the other side, data-driven prognostics approaches do not use any explicit physical model, but rely exclusively on the availability of process data related to equipment health to build (black-box) models that capture the degradation and failure modes of the equipment (Heng et al 2009; Van Tung & Yang 2009).…”