Self-tuning diagnosis of routine alarms in rotating plant items

Condition monitoring of rotating plant items in the energy generation industry is often achieved through examination of vibration signals. Engineers use this data to monitor the operation of turbine generators, gas circulators and other key plant assets. A common approach in such monitoring is to trigger an alarm when a vibration deviates from a predefined envelope of normal operation. This limit-based approach, however, generates a large volume of alarms not indicative of system damage or concern, such as operational transients that result in temporary increases in vibration. In the nuclear generation context, all alarms on rotating plant assets must be analysed and subjected to auditable review. The analysis of these alarms is often undertaken manually, on a case- by-case basis, but recent developments in monitoring research have brought forward the use of intelligent systems techniques to automate parts of this process. A knowledge- based system (KBS) has been developed to automatically analyse routine alarms, where the underlying cause can be attributed to observable operational changes. The initialisation and ongoing calibration of such systems, however, is a problem, as normal machine state is not uniform throughout asset life due to maintenance procedures and the wear of components. In addition, different machines will exhibit differing vibro- acoustic dynamics. This paper proposes a self-tuning knowledge-driven analysis system for routine alarm diagnosis across the key rotating plant items within the nuclear context common to the UK. Such a system has the ability to automatically infer the causes of routine alarms, and provide auditable reports to the engineering staff

A self-validating control system based approach to plant fault detection and diagnosis

An approach is proposed in which fault detection and diagnosis (FDD) tasks are distributed to separate FDD modules associated with each control system located throughout a plant. Intended specifically for those control systems that inherently eliminate steady state error, it is modular, steady state based, requires very little process specific information and therefore should be attractive to control systems implementers who seek economies of scale. The approach is applicable to virtually all types of process plant, whether they are open loop stable or not, have a type or class number of zero or not and so on. Based on qualitative reasoning, the approach is founded on the application of control systems theory to single and cascade control systems with integral action. This results in the derivation of cause-effect knowledge and fault isolation procedures that take into account factors like interactions between control systems, and the availability of non-control-loop-based sensors

Rancang Bangun Sistem Pendeteksian Penyakit Tanaman Anthurium Dengan Metode Variable-Centered Intellegent Rule System (VCIRS)

The anthurium plant disease detection system that has been built is a system that can help diagnose diseases in anthurium plants based on symptoms input by the user and provide solutions to these problems. This system is intended to provide easy access to information about the types of anthurium diseases and their treatment solutions for planters, anthurium enthusiasts, or nonexpert who really need this information. This system uses disease and treatment data sourced from anthurium plant experts. There are 13 diseases and 44 symptoms of disease which becomes the system knowledge base. The research methodology carried out includes the process of knowledge acquisition, knowledge representation, VCIRS design, system analysis and design, system implementation and testing. In this study, the anthurium plant detection system was tested 13 times. The trial results showed that the system was able to diagnose anthurium plant diseases with an accuracy rate of 92.3%. Errors occur because of the diagnosis of symptoms used in several diseases and it turns out to have a higher usage rate value in a VCIRS rule

TSA: an expert system for solid waste transfer station.

This paper presents the development of expert system to assist in the operation of solid waste transfer station. The knowledge based consists of a rule-based expert system for the diagnosis of site selection and problems of transfer station and subsequent identification of remedial control actions. Siting criteria are used to identifying and evaluating potential development sites. It is generally recognized that an expert system can cope with many of the common problems relative with the operation and site selection of solid waste transfer station. In this work an expert system is developed which supervises the site selection and problem of waste transfer station. The knowledge acquisition to develop this expert system involved an exhaustive literature review on waste transfer station operation plant and interviews with experienced plant operators. The development tool for this system is Kappa-PC. Keyword

Aluminium Process Fault Detection and Diagnosis

The challenges in developing a fault detection and diagnosis system for industrial applications are not inconsiderable, particularly complex materials processing operations such as aluminium smelting. However, the organizing into groups of the various fault detection and diagnostic systems of the aluminium smelting process can assist in the identification of the key elements of an effective monitoring system. This paper reviews aluminium process fault detection and diagnosis systems and proposes a taxonomy that includes four key elements: knowledge, techniques, usage frequency, and results presentation. Each element is explained together with examples of existing systems. A fault detection and diagnosis system developed based on the proposed taxonomy is demonstrated using aluminium smelting data. A potential new strategy for improving fault diagnosis is discussed based on the ability of the new technology, augmented reality, to augment operators’ view of an industrial plant, so that it permits a situation-oriented action in real working environments

Penerapan Case-based Reasoning (CBR) pada Sistem Pakar Diagnosis Penyakit Tanaman Pangan

This study discusses an expert system for diagnosing food plant diseases by applying Case-based Reasoning (CBR). CBR is a way of thinking about computer reasoning by utilizing past knowledge to handle new cases. CBR resolves new cases by observing the old cases that are closest to the new cases. The diagnosis stage begins with entering new cases with their symptoms to be diagnosed into the system, after that calculating the similarity value of new cases with existing cases on a case basis with the nearest neighbor algorithm. Based on testing using test data with a similarity threshold of 70%, the system produces performance with a sensitivity of 100% and an average accuracy rate of 76, 74%. This proves that the system works well for diagnosing food plant diseases (rice, cassava, corn, and peanuts).Abstrak - Penelitian ini membahas tentang sistem pakar diagnosis penyakit tanaman pangan dengan menerapkan Case-based Reasoning (CBR). CBR merupakan cara berpikir bernalar komputer dengan memanfaatkan pengetahuan yang lalu untuk menangani kasus baru. CBR menyelesaikan kasus baru  dengan mengamati  kasus lama yang terdekat dengan kasus baru. Tahapan diagnosis diawali dengan mengentrikan kasus baru dengan gejalanya yang akan didiagnosis ke dalam sistem, setelah itu menghitung nilai kemiripan kasus baru dengan kasus-kasus yang ada pada basis kasus dengan algoritma nearest neighbor. Berdasarkan pengujian menggunakan data uji dengan ambang kemiripan sebesar 70% maka sistem menghasilkan performa dengan sensitivitas 100% dan tingkat akurasi rata-rata 76, 74%. Hal ini membuktikan bahwa sistem bekerja dengan baik untuk mendiagnosis penyakit tanaman pangan (padi, singkong, jagung, dan kacang tanah)

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Linguistic OWA and time windows based Fault Identification in wide plants

Producción CientíficaFault detection and diagnosis in industrial processes are challenging tasks that demand effective and timely decision making procedures. The multivariate statistical approaches for fault detection based on data have been very useful. However, they are known to be less powerful for fault diagnosis because they normally require prior knowledge of the problem involved. In this context, this proposal is based on an on-line, distributed fault isolation approach to provide a scored rank of variables considered as respon- sible for the faults in a more robust and earlier way than usual approaches. The fault isolation is carried out considering some top Fault Isolation (FI) methods, without prior knowledge regarding faults, in a distributed and collaborative way by a linguistic based decision making. The isolation of faulty variables provided by each FI approach is aggregated to provide a fault identification based on a scored ranking at two time points: after the fault detection and when the plant has recovered. In both cases, the final fault isolation is provided as a scored ranking obtained by Ordered Weighted Average operators (OWA) and Regular Increasing Monotone (RIM) aggregation functions, which permit the implementation of linguistic aggregation functions. The risk aversion during this multicriteria isolation is tuned by the user and can provide several strategies or policies. The fault isolation at two key times searches for the origin of faults and evaluates the evolution of the system after the fault’s occurrence in the new working position of the plant. This is because faults in an industrial plant are propagated to different variables due to the actions of the process controllers. This method has been applied to two complex benchmark plants obtaining an earlier and more robust isolation.Este trabajo forma parte del proyecto de investogación: MINECO/FEDER. ref: DPI2015- 67341- C2-2-

Design of a Multi-Agent System for Process Monitoring and Supervision

New process monitoring and control strategies are developing every day together with process automation strategies to satisfy the needs of diverse industries. New automation systems are being developed with more capabilities for safety and reliability issues. Fault detection and diagnosis, and process monitoring and supervision are some of the new and promising growth areas in process control. With the help of the development of powerful computer systems, the extensive amount of process data from all over the plant can be put to use in an efficient manner by storing and manipulation. With this development, data-driven process monitoring approaches had the chance to emerge compared to model-based process monitoring approaches, where the quantitative model is known as a priori knowledge. Therefore, the objective of this research is to layout the basis for designing and implementing a multi-agent system for process monitoring and supervision. The agent-based programming approach adopted in our research provides a number of advantages, such as, flexibility, adaptation and ease of use. In its current status, the designed multi-agent system architecture has the three different functionalities ready for use for process monitoring and supervision. It allows: a) easy manipulation and preprocessing of plant data both for training and online application; b) detection of process faults; and c) diagnosis of the source of the fault. In addition, a number of alternative data driven techniques were implemented to perform monitoring and supervision tasks: Principal Component Analysis (PCA), Fisher Discriminant Analysis (FDA), and Self-Organizing Maps (SOM). The process system designed in this research project is generic in the sense that it can be used for multiple applications. The process monitoring system is successfully tested with Tennessee Eastman Process application. Fault detection rates and fault diagnosis rates are compared amongst PCA, FDA, and SOM for different faults using the proposed framework

Adaptive control with an expert system based supervisory level

Adaptive control is presently one of the methods available which may be used to control plants with poorly modelled dynamics or time varying dynamics. Although many variations of adaptive controllers exist, a common characteristic of all adaptive control schemes, is that input/output measurements from the plant are used to adjust a control law in an on-line fashion. Ideally the adjustment mechanism of the adaptive controller is able to learn enough about the dynamics of the plant from input/output measurements to effectively control the plant. In practice, problems such as measurement noise, controller saturation, and incorrect model order, to name a few, may prevent proper adjustment of the controller and poor performance or instability result. In this work we set out to avoid the inadequacies of procedurally implemented safety nets, by introducing a two level control scheme in which an expert system based 'supervisor' at the upper level provides all the safety net functions for an adaptive controller at the lower level. The expert system is based on a shell called IPEX, (Interactive Process EXpert), that we developed specifically for the diagnosis and treatment of dynamic systems. Some of the more important functions that the IPEX system provides are: (1) temporal reasoning; (2) planning of diagnostic activities; and (3) interactive diagnosis. Also, because knowledge and control logic are separate, the incorporation of new diagnostic and treatment knowledge is relatively simple. We note that the flexibility available in the system to express diagnostic and treatment knowledge, allows much greater functionality than could ever be reasonably expected from procedural implementations of safety nets. The remainder of this chapter is divided into three sections. In section 1.1 we give a detailed review of the literature in the area of supervisory systems for adaptive controllers. In particular, we describe the evolution of safety nets from simple ad hoc techniques, up to the use of expert systems for more advanced supervision capabilities