This article develops an optimum Condition-Based Maintenance (CBM) policy for continuously monitored degrading systems with multiple failure modes. The degradation of system state is described by a stochastic process, and a maintenance alarm is used to signal when the degradation reaches a threshold level. Unlike existing CBM models, this article considers multiple sudden failures that can occur during a system's degradation. The failure rate corresponding to each failure mode is influenced by either the age of the system, the state degradation of the system, or both. A joint model is constructed for the statistically dependent time-to-maintenance due to system degradation and time-to-failure of different failure modes. This model is then utilized to obtain the optimum maintenance threshold level that maximizes the system's limiting availability over its life cycle or minimizes the long-run cost per unit time. A numerical example, using real-life data from a reliability test of communication systems, is p...

Condition-based maintenance for continuously monitored degrading systems with multiple failure modes

Stator fault analysis of three-phase induction motors using information measures and artificial neural networks

An optimal condition-based maintenance policy for a degrading system subject to the competing risks of soft and hard failure

A new concept of the quadratic-nonlinearity power-index spectrum, QNLPI(f), that can be used in signal detection and classification, is proposed based on the bicoherence spectrum. The proposed QNLPI(f) is derived as a projection of the 3-D bicoherence spectrum into 2-D spectrum that quantitatively describes how much of the mean square power at a certain frequency f is generated by nonlinear quadratic interaction between different frequencies. The proposed index, QNLPI(f), can be used to simplify the study of bispectrum and bicoherence signal spectra. It also inherits useful characteristics from the bicoherence such as high immunity to additive gaussian noise, high capability of nonlinear-system identifications, and amplification invariance. Concept of the proposed index and its computational considerations are discussed first using computer-generated data and then applied to real-world vibration data from a helicopter drive train to assess health conditions of different mechanical faults as a part of condition-based maintenance.

Quadratic-Nonlinearity Index Based on Bicoherence and its Application in Condition Monitoring of Drive-Train Components

The operational performance of a chemical process plant highly depends on the assets’ condition and maintenance practices. As chemical processes are highly complex systems, increasing the risk frequencies and their interactions, the maintenance planning becomes crucial for stable operation. This paper provides a critical analysis of the recently developed approaches for asset maintenance approaches in the chemical industry. The strategies include corrective maintenance, time-based, risk-based, condition-based and opportunistic maintenance. Various methods on selecting the optimal maintenance strategy are discussed as well. This paper also evaluates reliability issues in chemical plants and integrated sites encompassing the maintenance optimisation. Several directions for potential future improvements are proposed based on this analysis, as follows: (i) potential study of exploiting production or other opportunities to postpone or conduct earlier maintenance; (ii) joint optimisation of spare part ordering strategy and data-driven maintenance planning study is needed; (iii) fault propagation modelling of structural dependent units to facilitate proper maintenance planning; (iv) a framework or tool that consider quantitative and qualitative time-variant data inputs is lacking for business-informed asset maintenance.

Asset maintenance optimisation approaches in the chemical and process industries - A review.

A new concept of nonparametric signal detection and classification technique is proposed using mutual information measures in the time-frequency domain. The time-frequency-based self-information and mutual information are defined in terms of the cross time-frequency distribution. Based on time-frequency mutual information theory, this paper presents applications of the proposed technique to real-world vibration data obtained from a dedicated condition-based-maintenance experimental test bed. Baseline, unbalanced, and misaligned experimental settings of helicopter drivetrain bearings and shafts are quantitatively distinguished by the proposed techniques. With imbalance quantifiable by variance in the in-phase mutual information and misalignment quantifiable by variance in the quadrature mutual information developed and presented herein, machine health classification can be accomplished by use of statistical bounding regions.

Advanced Time–Frequency Mutual Information Measures for Condition-Based Maintenance of Helicopter Drivetrains

The classical time-frequency distributions represent time- and frequency-localized energy. However, it is not an easy task to analyze multiple signals that have been simultaneously collected. In this paper, a new concept of non-parametric detection and classification of the signals is proposed using the mutual information measures in the time-frequency domain. The time-frequency-based self and mutual information is defined in terms of cross time-frequency distribution. Based on the time-frequency mutual information theory, this paper presents applications of the proposed technique to real-world vibration data. The baseline and misaligned experimental settings are quantitatively distinguished by the proposed technique.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Kwangik Cho

Papers

Advanced Time–Frequency Mutual Information Measures for Condition-Based Maintenance of Helicopter Drivetrains

Applications of Time-Frequency Analysis for Aging Aircraft Component Diagnostics and Prognostics