Machine condition monitoring using artificial intelligence: The incremental learning and multi-agent system approach

Machine condition monitoring is gaining importance in industry due to the need to increase machine reliability and decrease the possible loss of production due to machine breakdown. Often the data available to build a condition monitoring system does not fully represent the system. It is also often common that the data becomes available in small batches over a period of time. Hence, it is important to build a system that is able to accommodate new data as it becomes available without compromising the performance of the previously learned data. In real-world applications, more than one condition monitoring technology is used to monitor the condition of a machine. This leads to large amounts of data, which require a highly skilled diagnostic specialist to analyze. In this thesis, artificial intelligence (AI) techniques are used to build a condition monitoring system that has incremental learning capabilities. Two incremental learning algorithms are implemented, the first method uses Fuzzy ARTMAP (FAM) algorithm and the second uses Learn++ algorithm. In addition, intelligent agents and multi-agent systems are used to build a condition monitoring system that is able to accommodate various analysis techniques. Experimentation was performed on two sets of condition monitoring data; the dissolved gas analysis (DGA) data obtained from high voltage bushings and the vibration data obtained from motor bearing. Results show that both Learn++ and FAM are able to accommodate new data without compromising the performance of classifiers on previously learned information. Results also show that intelligent agent and multi-agent system are able to achieve modularity and flexibility

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

Development of a quantitative health index and diagnostic method for efficient asset management of power transformers

Power transformers play a very important role in electrical power networks and are frequently operated longer than their expected design life. Therefore, to ensure their best operating performance in a transmission network, the fault condition of each transformer must be assessed regularly. For an accurate fault diagnosis, it is important to have maximum information about an individual transformer based on unbiased measurements. This can best be achieved using artificial intelligence (AI) that can systematically analyse the complex features of diagnostic measurements. Clustering techniques are a form of AI that is particularly well suited to fault diagnosis. To provide an assessment of transformers, a hybrid k-means algorithm, and probabilistic Parzen window estimation are used in this research. The clusters they form are representative of a single or multiple fault categories. The proposed technique computes the maximum probability of transformers in each cluster to determine their fault categories. The main focus of this research is to determine a quantitative health index (HI) to characterize the operating condition of transformers. Condition assessment tries to detect incipient faults before they become too serious, which requires a sensitive and quantified approach. Therefore, the HI needs to come from a proportionate system that can estimate health condition of transformers over time. To quantify this condition, the General Regression Neural Network (GRNN), a type of AI, has been chosen in this research. The GRNN works well with small sets of training data and avoids the needs to estimate large sets of model parameters, following a largely non-parametric approach. The methodology used here regards transformers as a collection of subsystems and summarizes their individual condition into a quantified HI based on the existing agreed benchmarks drawn from IEEE and CIGRE standards. To better calibrate the HI, it may be mapped to a failure probability estimate for each transformer over the coming year. Experimental results of the research show that the proposed methods are more effective than previously published approaches when diagnosing critical faults. Moreover, this novel HI approach can provide a comprehensive assessment of transformers based on the actual condition of their individual subsystems

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Condition-based hazard rate estimation and optimal maintenance scheduling for electrical transmission system

The effectiveness of expending maintenance resources can vary dramatically depending on the target and timing of the maintenance activities. The objective of the work to develop a method of allocating economic resources and scheduling maintenance tasks among bulk transmission system equipment, so as to optimize the effect of maintenance with respect to the mitigation of component failure consequences. Techniques including condition-based failure rate estimation of electric transmission system components, analysis of failure consequences in power system, probabilistic modeling and risk assessment, and optimization are integrated in the work. Hidden Markov model is a good tool to estimate instantaneous status for deteriorating components. The maintenance selection and scheduling approach for bulk transmission equipment is based on the cumulative long-term risk caused by failure of each piece of equipment;This approach not only accounts for equipment failure probability and equipment damage, but it also accounts for the outage consequence in term of system related security problems. Various types of maintenance activities are studied and their relationship to the failure modes and system security improvement are investigated. An optimizer is developed to select and schedule the maintenance for large networks with various types of resource constraints, together with methods of resource reallocation;Finally, a strategy of incorporating maintenance activities among different transmission owners is developed. The objective of our work is to allocate resources economically and strategically so as to provide best performance of maintenance for electrical transmission system. These strategies can also be applied to problems inherent to resource intensive asset management in many similar types of infrastructures such as gas pipelines, airlines, and telecommunications

Leakage Detection And Containment In Arrangement 1 Seals

TutorialMechanical seals are the most common means of sealing industrial centrifugal pumps. There are a wide variety of seal options including the use of single or dual seal arrangements. While dual seals provide benefits in leakage containment and monitoring, single seals continue to be widely used due to their lower cost and simpler designs. Newer piping plans however offer options to increase leakage detectability and containment in single seals without sacrificing the simplicity of the seal design. A Plan 65 piping plan was introduced in API 682 Third Edition and ISO 21049. This captured leakage detection practices which were currently in use in the pipeline industry. The upcoming Fourth Edition of API 682 changes the designation of this plan to Plan 65A and adds an alternative version designated as Plan 65B. In addition, the Fourth Edition of API 682 will introduce Plan 66A and 66B which provide additional alternatives for monitoring and containing seal leakage in the seal gland. The selection of piping plans in any seal application depends not only on the application conditions but also on the expectations of the end user. This includes the design of the pump and also the infrastructure to monitor equipment performance and handle process leakage. With these new options for piping plans, end users can add additional capabilities to their existing Arrangement 1 seal installations and consider single seals for future applications