Condition monitoring

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

A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection

Abstract: This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which employs features or condition indicators derived from sensor data in combination with simple models of the system's degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.

Handbook of Condition Monitoring

Abstract: Need for Condition Monitoring and Maintenance Management in Industries. Cost Effective Benefits of Condition Monitoring. Condition Monitoring Techniques. Machinery Fault Diagnosis. Vibration Condition Monitoring. Condition Monitoring of Gear Systems. Condition Monitoring of Hydraulic Systems. Condition Monitoring of Pneumatic Systems. Condition Monitoring of Electrical Machinery. Condition Monitoring of Turbomachinery. Wear and Oil Debris Monitoring. Artificial Neural Networks in Condition Monitoring. Infrared Thermography in Condition Monitoring. Expert Systems in Condition Monitoring. Corrosion Monitoring. Environmental Monitoring - Legal Aspects. Cost Effective Benefits of Maintenance. Reliability Centred Maintenance. Total Productive Maintenance. Modern Maintenance Management Techniques.

A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis

Abstract: Prognostics involves the effective utilization of condition or performance-based sensor signals to accurately estimate the remaining lifetime of partially degraded systems and components. The rapid development of sensor technology, has led to the use of multiple sensors to monitor the condition of an engineering system. It is therefore important to develop methodologies capable of integrating data from multiple sensors with the goal of improving the accuracy of predicting remaining lifetime. Although numerous efforts have focused on developing feature-level and decision-level fusion methodologies for prognostics, little research has targeted the development of “data-level” fusion models. In this paper, we present a methodology for constructing a composite health index for characterizing the performance of a system through the fusion of multiple degradation-based sensor data. This methodology includes data selection, data processing, and data fusion steps that lead to an improved degradation-based prognostic model. Our goal is that the composite health index provides a much better characterization of the condition of a system compared to relying solely on data from an individual sensor. Our methodology was evaluated through a case study involving a degradation dataset of an aircraft gas turbine engine that was generated by the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS).

Open-Circuit Fault Diagnosis in PMSG Drives for Wind Turbine Applications

Abstract: Condition monitoring and fault diagnosis are currently considered crucial means to increase the reliability and availability of wind turbines and, consequently, to reduce the wind energy cost. With similar goals, direct-drive wind turbines based on permanent magnet synchronous generators (PMSGs) with full-scale power converters are an emerging and promising technology. Numerous studies show that power converters are a significant contributor to the overall failure rate of modern wind turbines. In this context, open-circuit fault diagnosis in the two power converters of a PMSG drive for wind turbine applications is addressed in this paper. A diagnostic method is proposed for each power converter, allowing real-time detection and localization of multiple open-circuit faults. The proposed methods are suitable for integration into the drive controller and triggering remedial actions. In order to prove the reliability and effectiveness of the proposed fault diagnostic methods, several simulation and experimental results are presented.

Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering

Abstract: Machine prognosis is a significant part of condition-based maintenance and intends to monitor and track the time evolution of a fault so that maintenance can be performed or the task can be terminated to avoid a catastrophic failure. A new prognostic method is developed in this paper using adaptive neuro-fuzzy inference systems (ANFISs) and high-order particle filtering. The ANFIS is trained via machine historical failure data. The trained ANFIS and its modeling noise constitute an mth-order hidden Markov model to describe the fault propagation process. The high-order particle filter uses this Markov model to predict the time evolution of the fault indicator in the form of a probability density function. An online update scheme is developed to adapt the Markov model to various machine dynamics quickly. The performance of the proposed method is evaluated by using the testing data from a cracked carrier plate and a faulty bearing. Results show that it outperforms classical condition predictors.