Classification Of Incipient Faults Research Articles

Detection and Classification of Incipient Faults in Three-Phase Power Transformer Using DGA Information and Rule-based Machine Learning Method

Three-phase transformers (TPT) play a significant and crucial function in the power networks in order to connect the sub-systems and deliver the electrical energy to final customers. The TPT are one of the most high-priced equipment in modern power networks, and therefore their working condition should be constantly monitored to prevent their breakdown, power outages and huge financial damage. Accordingly, this paper presents a hybrid method for detection and classification of incipient faults in TPT using dissolved gas analysis techniques (DGAT) information and rule-based machine learning method. In the developed method, the most informative and important items of DGAT data out of 14 items selected by association rules mining technique (ARMT) are employed as the input of adaptive neuro-fuzzy inference system (ANFIS). The ARMT is implemented to select the items, which have maximum information and can train the ANFIS more accurately. Furthermore, in order to enhance the accuracy of ANFIS and improve its robustness in different implementations, black widow optimization algorithm is applied for ANFIS training. In order to evaluate the performance of developed method on real issues, two industrial data collections obtained from Iran-Transfo Company chemical laboratory and Damavand power substations are used. The obtained results through MATLAB simulations proved that the developed method has high fault detection and classification accuracy, robust function in different implementations, short run time and simple structure.

Fault identification using a chain of decision trees in an electrical submersible pump operating in a liquid-gas flow

The monitoring of centrifugal pumps is essential for the suitable operation of several industrial applications. The reliability of petroleum artificial lifting systems that use Electrical Submersible Pumps (ESP) depends substantially on the performance of these pumps. ESP can operate subjected to severe operating conditions such as viscous and two-phase flow. In recent years, real-time technologies based on machine learning algorithms have gained importance due to the capability to take advantage of historical data for future predictions. The present work proposes a particular assembly of Classification and Regression Trees (CART) for the detection and classification of incipient faults in a pumping system. Experiments were carried out on a ten-stage ESP to simulate, monitor and label the faults. The pump worked at 1800, 2400, 3000 and 3500 rpm, with a two-phase liquid-gas mixture. The gas-phase was nitrogen, and the liquid-phase was a heavy oil with a viscosity between 200 and 1000 cP. The proposed methodology, named here as Chain of Decision Trees, observe the system behavior based on the monitoring of the pressure, flow, torque, and temperature only. The algorithm has two steps. The first determines whether the system is in a fault state; if it is, the second determines the type of fault. The failures considered were the unexpected closure of the choke valve, the input pressure decreasing, the fluid viscosity increasing and the gas flow rate increasing. The proposed approach intends to improve the balance in classification and the interpretation of the cause of failure. The Chain of Decision Trees and the Decision Tree were compared regarding the overall accuracy and the individual fault misclassification getting a reduction in individual misclassification and better comprehensibility for the Chain of Decision Tree.

Artificial Intelligence Based Technique for the Classification of Incipient Faults in Power Transformer

Power transformer has been identified as crucial and vital equipment in power system. Any disturbance such as faults will result in immense impact to the whole power system. This paper presents the development of an Evolutionary Programming (EP) – Taguchi Method (TM) – Artificial Neural Network (ANN) based technique for the classification of incipient faults in power transformer using Dissolved Gas Analysis (DGA) method based on historical industrial data. It involved the development of ANN model and embedding TM and EP as the optimization techniques in order to enhance the system accuracy and efficiency. ANN is a powerful computational technique that mimics how human brain process information. It has great ability to learn from experiences and examples, hence greatly suitable for classification, pattern recognition and forecasting purposes. In designing the ANN model, there are parameters which need to be chosen wisely. However, there is no systematic ways and guidelines to select the optimal ANN parameters. It is greatly dependent on the design knowledge and experiences of the experts. The process of finding suitable parameters is become difficult, tedious and time consuming, thus optimization technique is needed to overcome the shortcoming. In this study, TM and EP were employed as the optimization techniques to improve the ANN-based model. The findings obtained from the proposed technique have proved the effectiveness of both TM and EP in optimizing the ANN model. As a result, a reliable EP-TM-ANN based system has been successfully developed that can classify incipient faults in power transformer.

A cognitive system for fault prognosis in power transformers

The power transformer is one of the most critical and expensive equipments in an electric power system. If it is out of service in an unexpected way, the damage for both society and electric utilities is very significant. Over the last decades, many computational tools have been developed to monitor the ‘health’ of such an important equipment. The classification of incipient faults in power transformers via Dissolved Gas Analysis (DGA) is, for instance, a very well known technique for this purpose. In this paper we present an intelligent system based on cognitive systems for fault prognosis in power transformers. The proposed system combines both evolutionary and connectionist mechanisms into a hybrid model that can be an essential tool in the development of a predictive maintenance technology, to anticipate when any equipment fault might occur and to prevent or reduce unplanned reactive maintenance. The proposed procedure has been applied to real databases derived from chromatographic tests of power transformers found in the literature. The obtained results are fully described showing the feasibility and validity of the new methodology. The proposed system can help Transformer Predictive Maintenance programmes offering a low cost and highly flexible solution for fault prognosis.

Incipient fault classification using a combination of S-transform and Hilbert transform method

SUMMARY Incipient faults such as those caused by tree contacts are difficult to detect by conventional relaying algorithms. These faults impact distribution feeder reliability by advancing –over time– into a full-blown fault if not detected in time. This paper proposes a combined technique using S-transform technique and Hilbert transform to extract the features of incipient fault. A least square support vector machine classifier is developed based on the features so that incipient faults can be distinguished from the normal disturbances. The comparison of the results of different feature selection methods has been conducted. The simulation results show that the proposed feature selection method is feasible and suitable. Copyright © 2012 John Wiley & Sons, Ltd.

Challenges and obstacles in the application of acoustic emission to process machinery

Managing asset integrity is crucial for the cost-effective asset management of processing plants. Consequently, new techniques for detection and classification of incipient faults are continuously being sought by industry and developed by research and development professionals, both corporate and academic. Although acoustic emission (AE) testing for faults in static equipment has been used since the 1970s, its use as a monitoring technology for various machinery conditions has been poorly adopted by industry, despite a significant volume of work having been published over the past twenty years describing success in detecting numerous rotating and reciprocating machinery faults. Anecdotal evidence from industry suggests that many ‘tried and failed’. The authors believe that this is because applying AE monitoring to industrial plant is fraught with poorly documented challenges, obstacles, and limitations that must be well understood and overcome before any reported results can be replicated. Thus, to enable potential users to approach AE monitoring with more realistic expectations, the current paper discusses several such problems and suggests some available techniques for their management. After an initial introduction on the basics of AE specifically applied to machinery monitoring, issues are divided into generic problems and application-specific challenges.