Decision support with data-analysis methods in a nuclear power plant

Early fault detection is an important issue in nuclear industry. Methods based on self-organizing map (SOM) in dynamic systems are discussed and developed to help operators and plant experts in their decision making and used together with other methods. Visualization issues are in an important role in this research. Prototype systems are built to be able to test the basic principles. Five different studies are presented in detail. This report summarizes the test case 4 (TC4) "Decision support at a nuclear power plant" in NoTeS and NoTeS2 projects in TEKES MASI research program

Failure detection and separation in SOM based decision support

Failure management in process industry has difficult tasks. Decision support in control rooms of nuclear power plants is needed. A prototype that uses Self-Organizing Map (SOM) method is under development in an industrial project. This paper has focus on failure detection and separation. A literature survey outlines the state-of-the-art and reflects our study to related works. Different SOM visualizations are used. Failure management scenarios are carried out to experiment the methodology and the Man-Machine Interface (MMI). U-matrix trajectory analysis and quantization error are discussed more in detail. The experiments show the usefulness of the chosen approach. Next step will be to add more practical views by analyzing real and simulated industrial data with the control room tool and by feedback from the end users

Outline of a fault diagnosis system for a large-scale board machine

Global competition forces process industries to continuously optimize plant operation. One of the latest trends for efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most likely faulty components responsible for abnormal process behavior. One of the key elements here is fault analysis. The paper describes and briefly discusses also other development phases, process decomposition, and the selection of FDD methods. The paper ends with an FDD case study of a large-scale industrial board machine including a description of the fault analysis and FDD algorithms for the resulting focus areas. Finally, the testing and validation results are presented and discussed.Peer reviewe

Seismology - Responsibilities and requirements of a growing science. Part 2 - problems and prospects

Theoretical and applied seismology, earthquake engineering, earth structure, industrial uses, facilities, and underground nuclear explosion detectio

Instantaneous failure mode remaining useful life estimation using non-uniformly sampled measurements from a reciprocating compressor valve failure

One of the major targets in industry is minimisation of downtime and cost, and maximisation of availability and safety, with maintenance considered a key aspect in achieving this objective. The concept of Condition Based Maintenance and Prognostics and Health Management (CBM/PHM) , which is founded on the principles of diagnostics, and prognostics, is a step towards this direction as it offers a proactive means for scheduling maintenance. Reciprocating compressors are vital components in oil and gas industry, though their maintenance cost is known to be relatively high. Compressor valves are the weakest part, being the most frequent failing component, accounting for almost half maintenance cost. To date, there has been limited information on estimating Remaining Useful Life (RUL) of reciprocating compressor in the open literature. This paper compares the prognostic performance of several methods (multiple linear regression, polynomial regression, Self-Organising Map (SOM), K-Nearest Neighbours Regression (KNNR)), in relation to their accuracy and precision, using actual valve failure data captured from an operating industrial compressor. The SOM technique is employed for the first time as a standalone tool for RUL estimation. Furthermore, two variations on estimating RUL based on SOM and KNNR respectively are proposed. Finally, an ensemble method by combining the output of all aforementioned algorithms is proposed and tested. Principal components analysis and statistical process control were implemented to create T^2 and Q metrics, which were proposed to be used as health indicators reflecting degradation processes and were employed for direct RUL estimation for the first time. It was shown that even when RUL is relatively short due to instantaneous nature of failure mode, it is feasible to perform good RUL estimates using the proposed techniques

Failure analysis informing intelligent asset management

With increasing demands on the UK’s power grid it has become increasingly important to reform the methods of asset management used to maintain it. The science of Prognostics and Health Management (PHM) presents interesting possibilities by allowing the online diagnosis of faults in a component and the dynamic trending of its remaining useful life (RUL). Before a PHM system can be developed an extensive failure analysis must be conducted on the asset in question to determine the mechanisms of failure and their associated data precursors that precede them. In order to gain experience in the development of prognostic systems we have conducted a study of commercial power relays, using a data capture regime that revealed precursors to relay failure. We were able to determine important failure precursors for both stuck open failures caused by contact erosion and stuck closed failures caused by material transfer and are in a position to develop a more detailed prognostic system from this base. This research when expanded and applied to a system such as the power grid, presents an opportunity for more efficient asset management when compared to maintenance based upon time to replacement or purely on condition

Failure detection and separation in SOM based decision support

Failure management in process industry has difficult tasks. Decision support in control rooms of nuclear power plants is needed. A prototype that uses Self-Organizing Map (SOM) method is under development in an industrial project. This paper has focus on failure detection and separation. A literature survey outlines the state-of-the-art and reflects our study to related works. Different SOM visualizations are used. Failure management scenarios are carried out to experiment the methodology and the Man-Machine Interface (MMI). U-matrix trajectory analysis and quantization error are discussed more in detail. The experiments show the usefulness of the chosen approach. Next step will be to add more practical views by analyzing real and simulated industrial data with the control room tool and by feedback from the end users

Learning from accidents: Analysis of multi-attribute events and implications to improve design and reduce human errors

High-technology accidents are likely to occur under a complex interaction of multiple active failures and latent conditions, and recent major accidents investigations are increasingly highlighting the role of human error or human-related factors as significant contributors. Latent conditions might have long incubation periods, which implies that a number of design failures may be embedded in systems until human errors trigger an accident sequence. Consequently, there is a need to scrutinise the relationship between enduring design deficiencies and human erroneous actions as a conceivable way to minimise accidents. This study will tackle this complex problem by applying an artificial neural network approach to a proprietary multi-attribute accident dataset, in order to disclose multidimensional relationships between human errors and design failures. Clustering and data mining results are interpreted to offer further insight into the latent conditions embedded in design. Implications to support the development of design failure prevention schemes are then discussed