The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Based on machine learning techniques, this paper presents a novel intelligent fault diagnosis method, which is an integrated framework concerning reconstruction independent component analysis (RICA) and multiclass relevance vector machine (MRVM). In this method, the RICA is first used to automatically extract features from raw vibration signals. Then, the learned features are used as the input data...
Data-based fault diagnosis technology applied in chemical industry process has attracted great attention, in which the effective methods for visualizing the process variation are still challenging. The self-organizing map (SOM) is an unsupervised learning algorithm of neural network, which is presented to solve the visualization monitoring and fault diagnosis problem. The high-dimensional input space...
Bearing fault diagnosis has attracted significant attention over the past few decades. In this paper, Alpha-stable distribution was introduced for feature extraction from faulty bearing vibration signals. Such a non-Gaussian model can accurately describe statistical characteristic of bearing fault signals with impulsive behavior. After extracting feature vectors by Alpha-stable distribution parameters,...
Renewable energy sources akin to wind energy are profusely available without any limitation. Reliability of wind turbine is critical to extract maximum amount of energy from the wind. The vibration signals in wind turbines rotation parts are of universal non-Gasussian and nonstationarity and the fault samples are usually very limited. Aiming at these problems, this paper proposed a wind turbine fault...
The dissolved gas-in-oil analysis (DGA) is a prevailing methodology being widely used to detect incipient faults in power transformers. However various methods have been developed to interpret DGA results, they may sometimes fail to diagnose precisely. The incipient fault identification accuracy of various artificial intelligence (AI) based methodology is varied with variation of input variable. Thus,...
The fault diagnosis of train air-conditioning unit is becoming extremely necessity because train is often occupied with many passengers for a long time. However, limited diagnostic accuracy has become a bottleneck of train air-conditioning unit fault diagnosis. In the paper, two new fault diagnosis methods based on prior knowledge (PK) for train air-conditioning unit was proposed. First of all, taking...
Safety in machine applications requires tracking machine health during the time of operations. Anomaly detection techniques are used to model normal behavior of the machines and raise an alarm if any anomaly is observed. But traditional anomaly detection techniques do not identify type and severity of aberrance in terms of amplitude, pattern or both. Once the anomalous behavior is observed then fault...
Based on VC dimension theory and structural risk minimization principle of statistical learning theory, Support vector machine (SVM) has a prominent advantage in solving classification and fault prediction problems, specifically suitable for small sample, nonlinear and high dimensional pattern recognition problems. However, SVM is originally created for solving binary classification problems. The...
The majority of world energy is produced, consumed or transformed by rotating machines, like turbo-alternators, wind turbines, pumps, compressors… etc. Consequently, the reliability of the rotating machines, which can be the subject of breakdowns or dysfunctions in their times of use, is vital for a correct operation of the various industrial applications. In addition, for mostly configurations in...
As the development of modern industry, fault diagnosis technology is becoming one of the major issues. A novel fault diagnosis approach is presented for rolling bearing system based on support vector machine (SVM) in this paper. Data acquired for rotatory machinery has a large quantity as well as a high dimensionality, both principal component analysis (PCA) method and linear discriminant analysis...
Effective equipment fault diagnosis can assist to schedule the proper maintenance and reduce breakdown risks for realistic engineering systems. In this paper, a novel two-step precognitive maintenance framework is proposed to diagnose the equipment health conditions based on its real-time Condition Monitoring (CM). The synthetic minority over-sampling technique is implemented firstly to balance a...
With the increasing complexity of modern industrial processes and equipment, single fault diagnosis technology has failed to meet diagnostic needs. A complex diagnostic system which get together a variety of different technologies is the future development trend of fault diagnosis. According to a large number of characteristic information caused by difficult fault diagnosis, principal component analysis...
The paper presents a new fault diagnosis method for the intermediate frequency (IF) signal processing system of the vector network analyzer (VNA) based on dynamic fuzzy neural network (DFNN). This paper gives the structure of the fault diagnosis with three test points in one port first. Then for four different ports, it chooses the same method. The fault diagnosis is done by on-line self-organizing...
According to the large variety of data generated during the spacecraft test and fault diagnosis, this paper designs a multi class classification algorithm based on deep learning method. The algorithm uses the stack auto-Encoder to initialize the initial weights and offsets of the multi-layer neural network, and then monitor the parameters after the initialization with the gradient descent method....
One of the crucial requirements for the practical implementation of empirical diagnostic systems is the capability of handling missing data. This is done by resorting to missing data imputation techniques in a pre-processing module. The pre-processing module is a part of a previously developed diagnostic system which receives batches of residuals generated by a combined set of observers and progressively...
In the last years, numerous investigations have been made within the field of faults diagnosis in induction motors. Most of them use data obtained either from the time domain, through advanced techniques in the frequency domain or even by simulation tools. Some researchers have employed a considerable effort in designing sophisticated algorithms to achieve the best performance of the diagnosis system...
When binary tree SVM is used for multi-class fault diagnosis, inner-class distance or between-class distance is always used to decide the classification hierarchy, but these methods cannot take the comprehensive separability information between classes into account, which leads to decrease the accuracy of fault diagnosis easily, so an improved binary tree SVM method is proposed. Combining the separability...
Support vector machine has obtained more and more attentions as a new method of machine learning based on the statistic learning theory. At the same time, there are increasing concerns about the fault diagnosis for practical engineering systems. Firstly, many kinds of SVM algorithms will be introduced, such as LS-SVM, LSVM and PSVM and so on. Besides, the advantages and disadvantage of those methods...
The intelligent diagnosis technology has got a rapid development in the past few decades. Some new theories and methods have been applied to practice successfully, and obtained fruitful achievements. The artificial neural network leads to a new way for the research on fault diagnosis. It also provides a new scientific theory and thinking way for fault diagnosis. The intelligent fault diagnosis technology...
The fault diagnosis of hydraulic pump is always a challenging issue in the field of machinery fault diagnosis. Not only is manual feature extraction time-consuming and laborious, but also the diagnostic result is easily affected by subjective experience. The stacked autoencoders (SAE) which has powerful learning and representation ability is applied in hydraulic pump fault diagnosis, and it is directly...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.