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K-Nearest Neighbor (KNN) is a commonly used fault diagnosis method, which is based on Euclidean distance between samples to carry out fault diagnosis. The differences between the variables have a direct effect on the Euclidean distance, which affects the KNN fault diagnosis effect. After the dimensional normalization, there are also some problems such as the decrease of variable diversity, and the...
Sensors in industrial systems fault frequently leading to serious consequences regarding cost and safety. The authors propose support vector machine-based classifier with diverse time- and frequency-domain feature models to detect and classify these faults. Three different kernels, i.e., linear, polynomial, and radial-basis function, are employed separately to examine classifier's performance in each...
The modular multilevel converter with series and parallel connectivity was shown to provide advantages in several industrial applications. Its reliability largely depends on the absence of failures in the power semiconductors. We propose and analyze a fault-diagnosis technique to identify shorted switches based on features generated through wavelet transform of the converter output and subsequent...
In view of the support vector machine (SVM) model applied in vibrant fault diagnosis for hydro-turbine generating unit, it exists problems of parameter settings and classification-plane incline due to unequal sample, which leads to lower diagnosis accuracy. As a new bionic intelligent optimization algorithm for glowworm swarm optimization(GSO), it has the characteristics of strong versatility and...
In this paper, a reinforcement learning approach is proposed to detect unexpected faults, where the noise-to-signal ratio of the data series is minimized for achieving robustness. The model parameter is taken as a special action of the reinforcement learning, and the policy valuation and policy improvement are utilized to find the parameters, which can make the estimated model consistent to the real-time...
The main purpose of this work is to implement a new and accurate approach based on Time Domain Reflectometry (TDR) combined with Adaptive Neuro-Fuzzy Inference System (ANFIS) to solve the problem of soft faults detection and localization on complex wiring electric network. Firstly, the response of the transmission line is given by applying the Finite Difference Time Domain method (FDTD) on the transmission...
In order to solve the problems such as availability of data extraction, better local optimum, gradient to dissipate more efficiently, this paper presents a new method of power transformer fault diagnosis based on deep learning and Softmax classifier. Power transformer fault diagnosis model is established based on stacked auto-encoders and softmax regression, then each restricted boltzmann machine...
It is difficult to establish accurate mathematical models to describe the range extender electric vehicles due to the non-stationary, non-linear and interconnection of the monitoring signal sources resulted from the massive moving parts and complex architecture in range-extender. And the support vector machine (SVM) and other algorithms would lead to the destruction of the natural structure and the...
The dynamic and system reliability of driving system in battery electric vehicles (BEVs) highly depend on the fault diagnosis technology. In this paper, we provided a new data compression approach and validated it on a method based on neural network (NN) to detect both failures' types and degree in drive system. In time-/frequency domain several statistical features were extracted from signals acquired...
A power transformer fault diagnosis method based on Improved Particle Swarm Optimization and BP neural network is proposed. The particle swarm algorithm that used to optimize the parameters of the BP neural network is prone to “premature”. By optimizing the inertia weight, in the process of increasing the number of iterations, the inertia weight can be gradually reduced, and the algorithm can avoid...
In this paper, a multi — factor prediction model based on Radical Basis Function(RBF) neural network is proposed to accurately predict the temperature of rolling bearing. According to the factors that affect the rolling bearing, including load, speed, vibration, displacement, bearing temperature and ambient temperature, the working temperature of the rolling bearing is predicted by combining the historical...
A fault diagnosis model of gas pressure regulator based on deep belief networks (DBN) theory is proposed in this paper, which is applied to the fault diagnosis method of gas regulator. Through the DBN parameter setting, the distribution of the original data of the network architecture can be reassembled by the greedy unsupervised layering training algorithm in the depth network to reflect the more...
With the rapid development of society and technology, home service robot is becoming cheaper and smarter. Facing with the difficulties of aging and shortage of labor, we can use home service robot (HSR) as a good companion and servant. However, the security and reliability problems have become bottlenecks in this field. It is meaningful to do researches on fault diagnosis of HSR. Due to its excellent...
In this paper the problem of improving the reliability of nonlinear dynamic objects fault diagnosing is presented. Model-based diagnostics nonparametric identification method is used. Diagnostic models are constructed on the base of Volterra kernels wavelet transforms. The effectiveness of the suggested diagnostic models based on Volterra kernels wavelet transforms is analyzed on the basis of simulation...
With the single-tube and double-tube fault of seven-level converter, this paper presents a new way to learn the faults feature based on the deep neural network of sparse autoencoder. Sparse autoencoder is an unsupervised learning method, it can learn the feature information of the fault data according to training. The feature information is used to train the softmax classifier by softmax regression...
This paper suggests identification and classification of fault at the time of power swing by using decision tree approach for transmission system network. The power swing which occurs during switching in/out of heavy loads, after fault clearance condition etc causes change in active and reactive power. The suggested decision tree method initially retrieves the information about fault/ power swing...
This paper studies the simultaneous fault diagnosis of the main reducer in the automobile transmission system assembly based on vibration signals. A simultaneous fault diagnosis model based on Paired Relevance Vector Machine (Paired-RVM) is proposed for the simultaneous fault of the main reducer, and each binary sub-classifier is trained with single fault samples and then fused by a pairing strategy...
Fault diagnosis plays a crucial role to maintain healthy conditions in rotating machinery. This paper proposes a framework to detect new patterns of abnormal conditions in gearboxes, that would be associated to new faults. This is achieved through a Hybrid Heuristic Algorithm for Evolving Models in scenarios of Classification and Clustering (HHA-EMCC), which is a machine learning algorithm that can...
This paper proposes a regrouping particle swarm optimization-based neural network (RegPSONN) for rolling bearing fault diagnosis. The proposed method applied neural network for rolling bearing conditions classification, and regrouping particle swarm optimization (RegPSO) is utilized for network training, and ten time-domain feature parameters are selected to establish the input vector. To evaluate...
Strategic necessities to design and implement practical diagnostic systems are the abilities of incremental learning and diagnosing new class defects under non-stationary and class imbalance conditions. In this work, a hybrid ensemble scheme, named Learn++NCS, is adopted for diagnosing bearing defects in induction motors. This diagnostic scheme includes a feature extraction module and a hybrid ensemble...
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