Condition monitoring

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

Prognostic Health Management of Aircraft Power Generators

Abstract: In this paper, prognostic tools are developed to detect the onset of electrical failures in an aircraft power generator, and to predict the generator's remaining useful life (RUL). Focus is on the rotor circuit since failure mode, effects, and criticality analysis (FMECA) studies indicate that it is a high priority candidate for condition monitoring. A signature feature is developed and tested by seeded fault experiments to verify that the initial stages of rotor faults are observable under diverse generator load conditions. A tracking filter is used to assess the damage state and predict generator RUL. This information helps to avoid unexpected failures while reducing the overall life-cycle cost of the system.

Prediction of Machine Health Condition Using Neuro-Fuzzy and Bayesian Algorithms

Abstract: This paper proposes a novel approach for machine health condition prognosis based on neuro-fuzzy systems (NFSs) and Bayesian algorithms. The NFS, after training with machine condition data, is employed as a prognostic model to forecast the evolution of the machine fault state with time. An online model update scheme is developed on the basis of the probability density function (PDF) of the NFS residuals between the actual and predicted condition data. Bayesian estimation algorithms adopt the model's predicted data as prior information in combination with online measurements to update the degree of belief in the forecasting estimations. In order to simplify the implementation of the proposed approach, a recursive Bayesian algorithm called particle filtering is utilized to calculate in real time a posterior PDF by a set of random samples (or particles) with associated weights. When new data become available, the weights of all particles are updated, and then, predictions are carried out, which form the PDF of the predicted estimations. The developed method is evaluated via two experimental cases-a cracked carrier plate and a faulty bearing. The prediction performance is compared with three prevalent machine condition predictors-recurrent neural networks, NFSs, and recurrent NFSs. The results demonstrate that the proposed approach can predict machine conditions more accurately.

Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach

Abstract: Plastic bearings are widely used in medical applications, food processing industries, and semiconductor industries. However, no research on plastic bearing fault diagnostics using vibration sensors has been reported. In this paper, a two-step data mining-based approach for plastic bearing fault diagnostics using vibration sensors is presented. The two-step approach utilizes envelope analysis and empirical mode decomposition (EMD) to preprocess vibration signals and extract frequency domain and time domain fault features as condition indicators (CIs) for plastic bearing fault diagnosis. In the first step, the frequency domain CIs are used by a statistical classification model to identify bearing outer race faults. In the second step, the time domain CIs extracted using EMD are developed to build a k-nearest neighbor algorithm-based fault classifier to identify other types of bearing faults. Seeded fault tests on plastic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and real vibration signals are collected. The effectiveness of the presented fault diagnostic approach is validated using the plastic bearing seeded fault testing data.

A flexible algorithm for fault diagnosis in a centrifugal pump with corrupted data and noise based on ANN and support vector machine with hyper-parameters optimization

Abstract: Fault detection and diagnosis have an effective role for the safe operation and long life of systems. Condition monitoring is an appropriate way of the maintenance technique that is applicable in the fault diagnosis of rotating machinery faults. A unique flexible algorithm is proposed for classifying the condition of centrifugal pump based on support vector machine hyper-parameters optimization and artificial neural networks (ANNs) which are composed of eight distinct steps. Artificial neural networks (ANNs), support vector classification with genetic algorithm (SVC-GA) and support vector classification with particle swarm optimization (SVC-PSO) algorithm have been considered in a flexible algorithm to perform accurate classification in the manufacturing area. SVC-GA, SVC-PSO and ANN have been used together due to their importance and capabilities in classifying domain. Also, the superiority of the proposed hybrid algorithm (SVC with GA and PSO) is shown by comparing its results with SVC performance. Two types of faults through six features, flow, temperature, suction pressure, discharge pressure, velocity, and vibration, have been classified with proposed integrated algorithm. To test the robustness of the efficiency results of the proposed method, the ability of proposed flexible algorithm in dealing with noisy and corrupted data is analyzed.