Intelligent diagnosis for turbine blade faults using artificial neural networks and fuzzy logic
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Applications of machine learning to machine fault diagnosis: A review and roadmap
2020, Mechanical Systems and Signal ProcessingCriticality analysis of petrochemical assets using risk based maintenance and the fuzzy inference system
2019, Process Safety and Environmental ProtectionCitation Excerpt :The quantitative assessment of occupational safety risks is a safety feature (Pinto, 2014). The fuzzy inference system is the knowledge that describes the correct, timely or on-line diagnosis mechanism to detect pump failures (Azadeh et al., 2008) and turbine faults (Kuo, 1995). Ratnayake (2012) proposed a framework to integrate a large amount of data from different sources for optimal decision-making and inspection planning, and the focus is on static mechanical equipment in processes production plants.
A sparse auto-encoder-based deep neural network approach for induction motor faults classification
2016, Measurement: Journal of the International Measurement ConfederationCitation Excerpt :In the field of fault diagnosis, the majority of machine learning algorithms are supervised learning which needs a large amount of labeled data for training. Since neural networks possess strong representation ability due to its stacked hidden layers, they have been widely used as classifiers for machine fault diagnosis [10–12]. But the training of neural networks also needs a large amount of high-quality labeled data, and if the training samples are limited or cannot cover the testing distribution, the neural network may be easily overfitted which leads to a poor generalization especially for a complex classification problem.
Application of Genetic Fuzzy System for Damage Identification in Cantilever Beam Structure
2016, Procedia EngineeringDiagnostics of gear deterioration using EEMD approach and PCA process
2015, Measurement: Journal of the International Measurement ConfederationCitation Excerpt :Since the vibration measurements of the faulted machines normally exhibit high complexity, many researchers and engineers utilize the concepts of artificial intelligence and machine learning to identify the occurrence of machine malfunctions. Artificial neural network (ANN) and fuzzy logic techniques have been applied to classify the faults of gearbox systems and another rotating machinery [17,27–32]. Except for the ANN, the support vector machine (SVM) [15,20] and decision tree method [16,33] are also employed to classify the different fault types of gear transmission systems.
Application of a fuzzy inference system for functional failure risk rank estimation: RBM of rotating equipment and instrumentation
2014, Journal of Loss Prevention in the Process IndustriesCitation Excerpt :Basically, a fuzzy membership function (MF) plots values of a crisp range between 0 and 1, which enables the degree of vagueness or uncertainty that has been associated with conventional crisp sets to be represented (Sharma et al., 2005). Researchers such as Bragli et al. (2003), Bertolini et al. (2009), Guo et al. (2009), Kuo (1995), Li, Chen, Daib, and Lib (2010), Mure and Demichela (2009), Pinto (2014), Ratnayake (2013), Schwartz, Kaufman, and Schwartz (2004), Seneviratne and Ratnayake (2013, 2012) and Suresh and Mujumdar (2004) have proposed risk prediction approaches incorporating fuzzy set theory. This manuscript illustrates the use of a fuzzy set theory-based consistent approach, estimating the FFR, to enhance the effectiveness of RBM planning.