Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

A Neural Network Integrated Decision Support System for Condition-Based Optimal Predictive Maintenance Policy

Abstract: This paper develops an integrated neural-network-based decision support system for predictive maintenance of rotational equipment. The integrated system is platform-independent and is aimed at minimizing expected cost per unit operational time. The proposed system consists of three components. The first component develops a vibration-based degradation database through condition monitoring of rolling element bearings. In the second component, an artificial neural network model is developed to estimate the life percentile and failure times of roller bearings. This is then used to construct a marginal distribution. The third component consists of the construction of a cost matrix and probabilistic replacement model that optimizes the expected cost per unit time. Furthermore, the integrated system consists of a heuristic managerial decision rule for different scenarios of predictive and corrective cost compositions. Finally, the proposed system can be applied in various industries and different kinds of equipment that possess well-defined degradation characteristics

Adaptive modeling of changed states in predictive condition monitoring

Abstract: An improved empirical model-based surveillance or control system for monitoring or controlling a process or machine (105) provides adaptation of the empirical model (117) in response to new operational states that are deemed normal or non-exceptional for the process or machine. An adaptation decision module (125) differentiates process or sensor upset requiring alerts from new operational states not yet modeled. A retraining module (128) updates the empirical model (117) to incorporate the new states, and a pruning technique optionally maintains the empirical model by removing older states in favor of the added new states recognized by the model.

A novel technique for selecting mother wavelet function using an intelli gent fault diagnosis system

Abstract: This paper presents an optimized gear fault identification system using genetic algorithm (GA) to investigate the type of gear failures of a complex gearbox system using artificial neural networks (ANNs) with a well-designed structure suited for practical implementations due to its short training duration and high accuracy. For this purpose, slight-worn, medium-worn, and broken-tooth of a spur gear of the gearbox system were selected as the faults. In fault simulating, two very similar models of worn gear have been considered with partial difference for evaluating the preciseness of the proposed algorithm. Moreover, the processing of vibration signals has become much more difficult because a full-of-oil complex gearbox system has been considered to record raw vibration signals. Raw vibration signals were segmented into the signals recorded during one complete revolution of the input shaft using tachometer information and then synchronized using piecewise cubic hermite interpolation to construct the sample signals with the same length. Next, standard deviation of wavelet packet coefficients of the vibration signals considered as the feature vector for training purposes of the ANN. To ameliorate the algorithm, GA was exploited to optimize the algorithm so as to determine the best values for ''mother wavelet function'', ''decomposition level of the signals by means of wavelet analysis'', and ''number of neurons in hidden layer'' resulted in a high-speed, meticulous two-layer ANN with a small-sized structure. This technique has been eliminated the drawbacks of the type of mother function for fault classification purpose not only in machine condition monitoring, but also in other related areas. The small-sized proposed network has improved the stability and reliability of the system for practical purposes.

Gas Path Analysis Applied to Turbine Engine Condition Monitoring

Abstract: An approach is presented to turbine engine gas path analysis and monitoring, which permits the isolation of single or simultaneous multiple engine faults, with a quantitative assessment of their relative' severity. The software approach is described, showing features of its mathematical development and thermodynamic justification. Measureable engine parameters are treated as dependent variables, changes in which are mathematically interrelated to changes in component performance brought about by physical engine faults. Typical results are presented from real programs, wherein engine data were analyzed to provide meaningful and verified diagnoses of single and multiple engine faults.