• Journal of Ocean Engineering and Technology
• /
• v.16 no.5
• /
• pp.61-65
• /
• 2002

The fault detection and diagnosis of rotating machinery widely used in plants including the ship are important for maintaining the performance of Plants. Recently, the wavelet transform has been recognized an efficient method to detect a little variation of physical quantities by the synchronous localization of time and frequency domains using the translation and dilation of signals. In this Paper, In order to develop efficient and reliable fault detection and diagnosis system rotating machines, the performance of wavelet transformation to detect a little variation of machine status and neural network to diagnose the cause of machine faults are investigated and experimented.

• Journal of Electrical Engineering and Technology
• /
• v.12 no.2
• /
• pp.830-839
• /
• 2017

Support vector machine (SVM) is introduced as an effective fault diagnosis technique based on dissolved gases analysis (DGA) for oil-immersed transformers with maximum generalization ability; however, the applicability of the SVM is highly affected due to the difficulty of selecting the SVM parameters appropriately. Therefore, a novel approach combing SVM with improved imperialist competitive algorithm (IICA) for fault diagnosis of oil-immersed transformers was proposed in the paper. The improved ICA, which is proved to be an effective optimization approach, is employed to optimize the parameters of SVM. Cross validation and normalizations were applied in the training processes of SVM and the trained SVM model with the optimized parameters was established for fault diagnosis of oil-immersed transformers. Three classification benchmark sets were studied based on particle swarm optimization SVM (PSOSVM) and IICASVM with four multiple classification schemes to select the best scheme for transformer fault diagnosis. The results show that the proposed model can obtain higher diagnosis accuracy than other methods. The comparisons confirm that the proposed model is an effective approach for classification problems.

• Journal of the Institute of Convergence Signal Processing
• /
• v.20 no.2
• /
• pp.78-83
• /
• 2019

Sounds based machine fault diagnosis recovers all the studies that aim to detect automatically abnormal sound on machines using the acoustic emission by these machines. Conventional methods that use mathematical models have been found inaccurate because of the complexity of the industry machinery systems and the obvious existence of nonlinear factors such as noises. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose here an automatic fault diagnosis method of hand drills using discrete wavelet transform(DWT) and pattern recognition techniques such as artificial neural networks(ANN). We first conduct a filtering analysis based on DWT. The power spectral density(PSD) is performed on the wavelet subband except for the highest and lowest low frequency subband. The PSD of the wavelet coefficients are extracted as our features for classifier based on ANN the pattern recognition part. The results show that the proposed method can be effectively used not only to detect defects but also to various automatic diagnosis system based on sound.

• Tribology and Lubricants
• /
• v.18 no.2
• /
• pp.117-126
• /
• 2002

Abstract - An integrated condition monitoring algorithm based on an expert system was implemented in this work in order to monitor effectively the machine conditions. The knowledge base was consisted of numeric data which meant the posterior probability of each measurement parameter for the representative machine failures. Also the inference engine was constructed as a series of statistical process, where the probable machine fault was inferred by a mapping technology of pattern recognition. The proposed algorithm was, through the user interface, applied for an air compressor system where the temperature, vibration and wear properties were measured simultaneously. The result of the case study was found fairly satisfactory in the diagnosis of the machine condition since the predicted result was well correlated to the machine fault occurred.

• Journal of the Korean Society for Precision Engineering
• /
• v.14 no.11
• /
• pp.135-146
• /
• 1997

This paper describes fault diagnosis method in complex system with hierachical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement special neural network. Fault diagnosis system can forecast faults in a system and decide from current machine state signal information. Comparing with other diagnosis system for single fault, the developed system deals with multiple fault diagnosis comprising Hierarchical Neural Network(HNN). HNN consists of four level neural network, first level for item fault symptom classification, second level for item fault diagnosis, third level for component symptom classification, forth level for component fault diagnosis. UNIX IPC(Inter Process Communication) is used for implementing HNN wiht multitasking and message transfer between processes in SUN workstation with X-Windows(Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural newtork operate as a separate process in HNN. The message queue take charge of information exdhange and cooperation between each neural network.

• KEPCO Journal on Electric Power and Energy
• /
• v.4 no.2
• /
• pp.89-99
• /
• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• Journal of information and communication convergence engineering
• /
• v.17 no.2
• /
• pp.128-134
• /
• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.891-894
• /
• 2007

This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer. The aim of this study is to compare the performance of fault diagnosis based on AE signal and vibration signal with same load and speed. A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. In this study, component analysis was also performed to extract the feature and reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that extracted feature from AE sensor gave better performance in faults classification.

• Journal of the Korean Society of Industry Convergence
• /
• v.7 no.2
• /
• pp.199-205
• /
• 2004

This paper describes the fault diagnosis method to order to construct CIM in complex system with hierarchical structure similar to human body structure. Complex system is divided into unit, item and component. For diagnosing this hierarchical complex system, it is necessary to implement a special neural network. Fault diagnosis system can forecast faults in a system and decide from the signal information of current machine state. Comparing with other diagnosis system for a single fault, the developed system deals with multiple fault diagnosis, comprising hierarchical neural network (HNN). HNN consists of four level neural network, i.e. first is fault symptom classification and second fault diagnosis for item, third is symptom classification and forth fault diagnosis for component. UNIX IPC is used for implementing HNN with multitasking and message transfer between processes in SUN workstation with X-Windows (Motif). We tested HNN at four units, seven items per unit, seven components per item in a complex system. Each one neural network represents a separate process in UNIX operating system, information exchanging and cooperating between each neural network was done by message queue.

• KNOM Review
• /
• v.24 no.1
• /
• pp.13-19
• /
• 2021

In recent, there is research to maximize production by preventing failures/accidents in advance through fault diagnosis/prediction and factory automation in the industrial field. Cloud technology for accumulating a large amount of data, big data technology for data processing, and Artificial Intelligence(AI) technology for easy data analysis are promising candidate technologies for accomplishing this. Also, recently, due to the development of fault diagnosis/prediction, the equipment maintenance method is also developing from Time Based Maintenance(TBM), being a method of regularly maintaining equipment, to the TBM of combining Condition Based Maintenance(CBM), being a method of maintenance according to the condition of the equipment. For CBM-based maintenance, it is necessary to define and analyze the condition of the facility. Therefore, we propose a machine learning-based system and data model for diagnosing the fault in this paper. And based on this, we will present a case of predicting the fault occurrence in advance.