Condition monitoring

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

Intelligent Bearing Fault Diagnosis Method Combining Compressed Data Acquisition and Deep Learning

Abstract: Effective intelligent fault diagnosis has long been a research focus on the condition monitoring of rotary machinery systems. Traditionally, time-domain vibration-based fault diagnosis has some deficiencies, such as complex computation of feature vectors, excessive dependence on prior knowledge and diagnostic expertise, and limited capacity for learning complex relationships in fault signals. Furthermore, following the increase in condition data, how to promptly process the massive fault data and automatically provide accurate diagnosis has become an urgent need to solve. Inspired by the idea of compressed sensing and deep learning, a novel intelligent diagnosis method is proposed for fault identification of rotating machines. In this paper, a nonlinear projection is applied to achieve the compressed acquisition, which not only reduces the amount of measured data that contained all the information of faults but also realizes the automatic feature extraction in transform domain. For exploring the discrimination hidden in the acquired data, a stacked sparse autoencoders-based deep neural network is established and performed with an unsupervised learning procedure followed by a supervised fine-tuning process. We studied the significance of compressed acquisition and provided the effects of key factors and comparison with traditional methods. The effectiveness of the proposed method is validated using data sets from rolling element bearings and the analysis shows that it is able to obtain high diagnotic accuracies and is superior to the existing methods. The proposed method reduces the need of human labor and expertise and provides new strategy to handle the massive data more easily.

Prognostics, the real issues involved with predicting life remaining

Abstract: This paper reviews the fundamentals of prognostics with emphasis on the estimation of remaining life and the interrelationships between accuracy, precision and confidence. A distinction is made between the static view of failure distributions derived from historical data and the dynamic view of remaining life derived from condition. The nonstationary nature of prognoses is illustrated using data from a failing SH-60 helicopter gearbox. A method is demonstrated that measures the accuracy and uncertainty of remaining life estimates using example prognostic features. This method isolates the uncertainty attributable to features and their interpretation from the uncertainty due to the random variables that govern the physics of component failure. Results from the example features support a hypothesized trend of improved accuracy and lower uncertainty as remaining life decreases.

Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN)

Abstract: An efficient predictive plan is needed for any industry because it can optimize the resources management and improve the economy plant, by reducing unnecessary costs and increasing the level of safety. A great percentage of breakdowns in the productive processes are caused for gear box, they began its deterioration from early stages, also called incipient level. The extracted features from the DWT are used as inputs in a neural network for classification purposes. The results show that the developed method can reliably diagnose different conditions of the gear box. The wavelet transform is used to represent all possible types of transients in vibration signals generated by faults in a gear box. It is shown that the transform provides a powerful tool for condition monitoring and fault diagnosis. The vibration signal of a spur bevel gear box in different conditions is used to demonstrate the application of various wavelets in feature extraction. In this paper fault diagnostics of spur bevel gear box is treated as a pattern classification problem. The major steps in pattern classification are feature extraction, and classification. This paper investigates the use of discrete wavelets for feature extraction and artificial neural network for classification.

Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description

Abstract: This paper proposes a system for wind turbine condition monitoring using Adaptive Neuro-Fuzzy Interference Systems (ANFIS). For this purpose: (1) ANFIS normal behavior models for common Supervisory Control And Data Acquisition (SCADA) data are developed in order to detect abnormal behavior of the captured signals and indicate component malfunctions or faults using the prediction error. 33 different standard SCADA signals are used and described, for which 45 normal behavior models are developed. The performance of these models is evaluated in terms of the prediction error standard deviations to show the applicability of ANFIS models for monitoring wind turbine SCADA signals. The computational time needed for model training is compared to Neural Network (NN) models showing the strength of ANFIS in training speed. (2) For automation of fault diagnosis Fuzzy Interference Systems (FIS) are used to analyze the prediction errors for fault patterns. The outputs are both the condition of the component and a possible root cause for the anomaly. The output is generated by the aid of rules that capture the existing expert knowledge linking observed prediction error patterns to specific faults. The work is based on continuously measured wind turbine SCADA data from 18 turbines of the 2 MW class covering a period of 30 months. The system proposed in this paper shows a novelty approach with regard to the usage of ANFIS models in this context and the application of the proposed procedure to a wide range of SCADA signals. The applicability of the set up ANFIS models for anomaly detection is proved by the achieved performance of the models. In combination with the FIS the prediction errors can provide information about the condition of the monitored components. In this paper the condition monitoring system is described. Part two will entirely focus on application examples and further efficiency evaluation of the system.

Fault classification and fault signature production for rolling element bearings in electric machines

Abstract: Most condition monitoring techniques for rolling element bearings are designed to detect the four characteristic fault frequencies This has lead to the common practice of categorizing bearing faults according to fault location (ie, inner race, outer race, ball, or cage fault) While the ability to detect the four characteristic fault frequencies is necessary, this approach neglects another important class of faults that arise in many industrial settings This research introduces the notion of categorizing bearing faults as either single-point defects or generalized roughness These classes separate bearing faults according to the fault signatures that are produced rather than by the physical location of the fault Specifically, single-point defects produce the four predictable characteristic fault frequencies while faults categorized as generalized roughness produce unpredictable broadband changes in the machine vibration and stator current Experimental results are provided from bearings failed in situ via a shaft current These results illustrate the unpredictable and broadband nature of the effects produced by generalized roughness bearing faults This issue is significant because a successful bearing condition monitoring scheme must be able to reliably detect both classes of faults