Topic
Condition monitoring
About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.
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01 Jan 2010
TL;DR: A review of state-of-the-art predictive fault detection and diagnosis methods can be found in this article, where some very different generic models have been tailored to the various types of railway point mechanisms that are in use worldwide.
Abstract: Early attempts at monitoring the condition of railway point mechanisms employed simple thresholding techniques to detect faults, but success was limited and there were large numbers of false alarms and missed failures in the field. More recent research using data collected from line-side equipment and lab-based test rigs, though, is suggesting that it should indeed be possible to predict failures with sufficient accuracy and notice to be of genuine use to infrastructure maintainers and owners. This review into state-of-the-art predictive fault detection and diagnosis methods shows how some very different generic models have been tailored to the various types of mechanisms that are in use worldwide. In any specific case, the most appropriate combination of quantitative and qualitative techniques will be determined by the inherent failure modes of the system and the particular conditions under which it operates. Furthermore, it is vital to have a priori knowledge of the symptoms that are observable under fa...
65 citations
TL;DR: A novel approach based on artificial neural networks to reliability centred maintenance is presented, employed for false alarm detection and prioritization, training the artificial neural Networks over the time to increase the system reliability.
65 citations
01 Nov 2004
TL;DR: The feed-cutting force is estimated using inexpensive current sensors installed on the ac servomotor of a computerized numerical control (CNC) turning center, with the results applied to the intelligent tool wear monitoring system.
Abstract: It is very important to use a reliable and inexpensive sensor to obtain useful information about manufacturing processing, such as cutting force for monitoring automated machining. In this paper, the feed-cutting force is estimated using inexpensive current sensors installed on the ac servomotor of a computerized numerical control (CNC) turning center, with the results applied to the intelligent tool wear monitoring system. The mathematical model is used to disclose the implicit dependency of feed-cutting force on feed-motor current and feed speed. Afterwards, a neuro-fuzzy network is used to identify the cutting force with current measurement only. This hybrid math-fuzzy approach will reduce the modeling uncertainty and measurement cost. Finally, the estimated cutting force is applied in the tool-wear monitoring process. Successful experiments demonstrate robustness and effectiveness of the suggested method in the wide range of tool-wear monitoring applications.
65 citations
TL;DR: The idea of deep learning is introduced into wind turbine condition monitoring with the purpose of artificial intelligence monitoring and over-temperature fault warning of the high-speed side of bearing is realized efficiently and conveniently.
Abstract: Wind turbines condition monitoring and fault warning have important practical value for wind farms to reduce maintenance costs and improve operation levels. Due to the increase in the number of wind farms and turbines, the amount of data of wind turbines have increased dramatically. This problem has caused a need for efficiency and accuracy in monitoring the operating condition of the turbine. In this paper, the idea of deep learning is introduced into wind turbine condition monitoring. After selecting the variables by the method of the adaptive elastic network, the convolutional neural network (CNN) and the long and short term memory network (LSTM) are combined to establish the logical relationship between observed variables. Based on training data and hardware facilities, the method is used to process the temperature data of gearbox bearing. The purpose of artificial intelligence monitoring and over-temperature fault warning of the high-speed side of bearing is realized efficiently and conveniently. The example analysis experiments verify the high practicability and generalization of the proposed method.
65 citations
TL;DR: In this article, different works on eccentricity diagnosis of induction machines with different types of supply have been reviewed, and the importance of eccentricity fault diagnosis in wind turbines as doubly-fed induction generator (DFIG) is also reviewed.
Abstract: Induction machines (IMs) are widely used in different applications. Unpredicted breakdown of these machines usually leads to costly downtimes and repairs. These expenses can be minimized using proper condition monitoring techniques. Eccentricity fault is one of the widespread faults causing machine malfunction; its detection could be useful for prevention of harmful consequences. In this paper, different works on eccentricity diagnosis of IMs with different types of supply have been reviewed. It commences from the simplest open-loop machine, and by considering torque variation and inverter switching gradually turns to complicated closed-loop machine with different control strategies. While in most cases, current is used for condition monitoring, in some instances power and voltage are employed for fault diagnosis. Due to extensive use of IMs in wind turbines as doubly-fed induction generator (DFIG), in addition to declaration of importance of eccentricity fault diagnosis in DFIG, detection of eccentricity fault in DFIG is also reviewed.
65 citations