An agent-based implementation of hidden Markov models for gas turbine condition monitoring

This paper considers the use of a multi-agent system (MAS) incorporating hidden Markov models (HMMs) for the condition monitoring of gas turbine (GT) engines. Hidden Markov models utilizing a Gaussian probability distribution are proposed as an anomaly detection tool for gas turbines components. The use of this technique is shown to allow the modeling of the dynamics of GTs despite a lack of high frequency data. This allows the early detection of developing faults and avoids costly outages due to asset failure. These models are implemented as part of a MAS, using a proposed extension of an established power system ontology, for fault detection of gas turbines. The multi-agent system is shown to be applicable through a case study and comparison to an existing system utilizing historic data from a combined-cycle gas turbine plant provided by an industrial partner

On-line transformer condition monitoring through diagnostics and anomaly detection

This paper describes the end-to-end components of an on-line system for diagnostics and anomaly detection. The system provides condition monitoring capabilities for two in- service transmission transformers in the UK. These transformers are nearing the end of their design life, and it is hoped that intensive monitoring will enable them to stay in service for longer. The paper discusses the requirements on a system for interpreting data from the sensors installed on site, as well as describing the operation of specific diagnostic and anomaly detection techniques employed. The system is deployed on a substation computer, collecting and interpreting site data on-line

Improving root cause analysis through the integration of PLM systems with cross supply chain maintenance data

The purpose of this paper is to demonstrate a system architecture for integrating Product Lifecycle Management (PLM) systems with cross supply chain maintenance information to support root-cause analysis. By integrating product-data from PLM systems with warranty claims, vehicle diagnostics and technical publications, engineers were able to improve the root-cause analysis and close the information gaps. Data collection was achieved via in-depth semi-structured interviews and workshops with experts from the automotive sector. Unified Modelling Language (UML) diagrams were used to design the system architecture proposed. A user scenario is also presented to demonstrate the functionality of the system

A text Ontology Method based on mining Develop D –MATRIX

In this issue, we demonstrate a text mining method of ontology based on the development and updating of a D-matrix naturally extraction of a large number of verbatim repairs (written in unstructured text) collected during the analysis stages. dependence (D) Fault - Matrix is a systematic demonstrative model is used to capture data symptomatic level progressive elimination system including dependencies between observable symptoms and failure modes associated with a frame. Matrix is a time D-long process. The development of D-matrix from first standards and update using the domain information is a concentrated work. In addition, increased D-die time for the disclosure of new symptoms and failure modes observed for the first race is a difficult task. In this methodology, we first develop the fault diagnosis ontology includes concepts and relationships regularly seen in fault diagnosis field. Then we use text mining algorithm that make use of this ontology to distinguish basic items, such as coins, symptoms, failure modes, and conditions of the unstructured text verbatim repair. The proposed technique is tools like a prototype tool and accepted using real - life information collected from cars space

Development of Graph from D-matrix based on Ontological Text Mining Method

Fault dependency (D-matrix) is a diagnostic model that catches the fault system data and its causal relationship at the hierarchical system-level. It consists of dependencies and relationship between observable failure modes and symptoms associated with a system. Constructing such D-matrix fault detection model is time consuming task. In this paper, a system is proposed that describes an ontology based text mining method for automatically constructing D-matrix by mining hundreds of repair verbatim text data (typically written in unstructured text) collected during the diagnosis episodes. First we construct fault diagnosis ontology and then text mining techniques are applied to identify dependencies among failure modes and observable symptom. D-matrix is represented in graph so that analysis gets easier and faulty parts becomes easily detectable. The proposed method will be implemented as a prototype tool and validated by using real-life data collected from the automobile domain. DOI: 10.17762/ijritcc2321-8169.15055

Graphical Analysis on Text Mining Unstructured Data Using D-Matrix

Fault dependency (D-matrix) is used as a diagnostic model that identifies the fault system data and its causal relationship at the hierarchical system-level. It consists of dependencies and relationship between identified failure modes and symptoms related to a system. Constructing such D-matrix fault detection model is time overwhelming task .A system is proposed that describes associate ontology based text mining on unstructured data using D-matrix for automatically constructing D-matrix by mining many repair verbatim text data (typically written in unstructured text) collected throughout the identification process. And also graphical model generation for each generated D-matrix. Initially we construct fault diagnosis ontology and then text mining techniques are applied to spot dependencies among failure modes and identified symptom. D-matrix is represented in graph so analysis gets easier and faulty parts becomes simply detectable. The proposed methodology are implemented as a prototype tool and validated by using real-life information collected from the automobile domain

Semantic data mining and linked data for a recommender system in the AEC industry

Even though it can provide design teams with valuable performance insights and enhance decision-making, monitored building data is rarely reused in an effective feedback loop from operation to design. Data mining allows users to obtain such insights from the large datasets generated throughout the building life cycle. Furthermore, semantic web technologies allow to formally represent the built environment and retrieve knowledge in response to domain-specific requirements. Both approaches have independently established themselves as powerful aids in decision-making. Combining them can enrich data mining processes with domain knowledge and facilitate knowledge discovery, representation and reuse. In this article, we look into the available data mining techniques and investigate to what extent they can be fused with semantic web technologies to provide recommendations to the end user in performance-oriented design. We demonstrate an initial implementation of a linked data-based system for generation of recommendations

A survey on the development status and application prospects of knowledge graph in smart grids

With the advent of the electric power big data era, semantic interoperability and interconnection of power data have received extensive attention. Knowledge graph technology is a new method describing the complex relationships between concepts and entities in the objective world, which is widely concerned because of its robust knowledge inference ability. Especially with the proliferation of measurement devices and exponential growth of electric power data empowers, electric power knowledge graph provides new opportunities to solve the contradictions between the massive power resources and the continuously increasing demands for intelligent applications. In an attempt to fulfil the potential of knowledge graph and deal with the various challenges faced, as well as to obtain insights to achieve business applications of smart grids, this work first presents a holistic study of knowledge-driven intelligent application integration. Specifically, a detailed overview of electric power knowledge mining is provided. Then, the overview of the knowledge graph in smart grids is introduced. Moreover, the architecture of the big knowledge graph platform for smart grids and critical technologies are described. Furthermore, this paper comprehensively elaborates on the application prospects leveraged by knowledge graph oriented to smart grids, power consumer service, decision-making in dispatching, and operation and maintenance of power equipment. Finally, issues and challenges are summarised.Comment: IET Generation, Transmission & Distributio