Condition-based Maintenance System Research Articles

Machine Fault Diagnosis and Prognosis: The State of The Art

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.

Comparison of genetic and binary particle swarm optimization algorithms on system maintenance scheduling using prognostics information

Recent technical advances in condition-based maintenance technology have made it possible to not only diagnose existing failures, but also forecast future failures, which is called prognostics. A common method of maintenance scheduling in condition-based maintenance is to apply thresholds to prognostics information, which is not appropriate for systems consisting of multiple serially connected machinery. Maintenance scheduling is defined as a binary optimization problem and has been solved with a genetic algorithm. In this article, various binary particle swarm optimization methods are analysed and compared with each other and a genetic algorithm on a maintenance-scheduling problem for condition-based maintenance systems using prognostics information. The trade-off between maintenance and failure is quantified as the risk to be minimized. The forecasted failure probability of serially connected machinery is utilized in the analysis of the whole system. In addition to the comparison of a genetic algorithm and binary particle swarm optimization methods, a new binary particle swarm optimization that combines the good sides of two binary particle swarm optimizations is presented.

Development of Web Based Power Plant Maintenance Management System

For prevention of failure and extension of maintenance interval, many Asian power companies have continuously tried to apply Condition Based Maintenance system to their plants. However, only few companies succeed to change their maintenance system from fixed time based to equipment condition based, although there are many brand new ITs, such as high speed computing system, huge storage device, and advanced development software tools. This paper describes the reasons why they failed to change and proposes the solutions to construct maintenance management system based on equipment condition. The solutions were also applied practically to power plant in Korea.

A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems

This paper presents a framework for online reliability estimation of physical systems utilising degradation signals. Most prognostics methods promoted in the literature for estimation of mean-residual-life of individual components utilise trending or forecasting models in combination with mechanistic or empirical failure definition models. In the absence of sound knowledge for the mechanics of degradation and/or adequate failure data, it is not possible to establish practical failure definition models. However, if there exist domain experts with strong experiential knowledge, one can establish fuzzy inference models for failure definition. This paper presents a neuro-fuzzy approach for performing prognostics under such circumstances. The proposed approach is evaluated on a cutting tool monitoring problem. In particular, the method is used to monitor high-speed-steel drill-bits used for drilling holes in stainless steel metal plates.

Inspection of composite and metal adhesive bonds with an electrochemical sensor

An in-situ sensor, based on Electrochemical Impedance Spectroscopy (EIS), has been used to monitor the health of adhesive bonds constructed from various combinations of aluminum, graphite/epoxy, glass/epoxy, glass/polyester, and glass/vinylester composites and exposed to high humidity and temperature conditions. Modeling of the EIS data as an electric circuit demonstrated that several circuit parameters of the impedance spectra were sensitive to bond performance, as determined by wedge tests and lap shear tests. Moisture absorption by the adhesive and composite was calculated from the circuit capacitance, which was also a function of bonded area and bondline thickness (bondline + composite thickness for glass composites). Material differences, including saturation level of moisture, rate of absorption, and bondline stability, were readily seen among the various materials sets. The sensor electrodes are attached to opposite sides of a bond after fabrication, i.e. they are not embedded. Thus, they are suitable for monitoring existing bonded structures. They have the potential to identify bondlines that are in the early stages of degradation, prior to significant loss of bond strength. As an input to a condition-based maintenance system, they would identify weakening bondlines and allow preventative action to be scheduled and performed.

Development of a System for Detecting Abnormal Vibration during High-Speed Winding

The purpose of this research is to propose a new system, for detecting abnormal vibration during high-speed winding. The system gives a warning when the abnormality vibration of a winder has reached a certain level, or stops only the winder with the problem before the winding yarn package is broken.The research analyzes the data obtained from the operating winder, and compares with data acquired from he overhauled winders that were re-assembled and re-adjusted. The results of the analysis have validated the effectiveness and practicability of the system that systematically detects the level of abnormality in the winders to prevent a costly accident. And it is possible to adopt the Preventive Maintenance of winders by the Condition Based Maintenance System based on this system.

高速巻取機の振動監視システムの開発

The purpose of this research is to propose the new system, which focuses on detecting abnormal vibration during high-speed winding . When abnormality is detected in the vibration of a winder, it. gives a warning when the abnormality hasreached to the certain level, or stops only the winder with the problem before the winding yarn package is broken tomake a sudden, abnormal stop.The research analyzes the data obtained from the system as well as the overhauled winders, and makes comparisonwith the data acquired from the winders that were re-assembled and re-adjusted. The results of the analysis have validatedthe effectiveness and practicability of the system that systematically detects the level of abnormality from thewinders to prevent a costly accident. And it is possible to adopt the Preventive Maintenance of winders by the ConditionBased Maintenance System based on this system.

Computerized breast measurement from conception to weaning: clinical implications.

The CBM system has enabled our laboratory to measure breast growth and demonstrate the importance of the short-term local control of milk synthesis in lactating women. Although the specific mechanism by which the short-term control of milk synthesis occurs has yet to be fully understood, it is now apparent that the interaction between storage capacity, degree of fullness, and frequency of milk removal plays a significant role. These factors demonstrate that the breastfeeding mother can take comfort in the individuality of her breast development and feeding pattern, which is uniquely adapted to suit the physiology of her breasts and the developmental requirements of her infant.

The Negative Information Problem in Mechanical Diagnostics

Condition-based maintenance (CBM) is an emerging technology, which seeks to develop sensors and processing systems aimed at monitoring the operation of complex machinery such as turbine engines, rotor craft drivetrains, and industrial equipment. The goal of CBM systems is to determine the state of the equipment (i.e., the mechanical health and status), and to predict the remaining useful life for the system being monitored. The success of such systems depends upon a number of factors, including: (1) the ability to design or use robust sensors for measuring relevant phenomena such as vibration, acoustic spectra, infrared emissions, oil debris, etc.; (2) real-time processing of the sensor data to extract useful information (such as features or data characteristics) in a noisy environment and to detect parametric changes that might be indicative of impending failure conditions; (3) fusion of multi-sensor data to obtain improved information beyond that available to a single sensor; (4) micro and macro level models, which predict the temporal evolution of failure phenomena; and finally, (5) the capability to perform automated approximate reasoning to interpret the results of the sensor measurements, processed data, and model predictions in the context of an operational environment. The latter capability is the focus of this paper. Although numerous techniques have emerged from the discipline of artificial intelligence for automated reasoning (e.g., rule-based expert systems, blackboard systems, case-based reasoning, neural networks, etc.), none of these techniques are able to satisfy all of the requirements for reasoning about condition-based maintenance. This paper provides an assessment of automated reasoning techniques for CBM and identifies a particular problem for CBM, namely, the ability to reason with negative information (viz., data which by their absence are indicative of mechanical status and health). A general architecture is introduced for CBM automated reasoning, which hierarchically combines implicit and explicit reasoning techniques. Initial experiments with fuzzy logic are also described.