A new adaptive Mamdani-type fuzzy modeling strategy for industrial gas turbines

The paper presents a new system identification methodology for industrial systems. Using the original Mamdani fuzzy rule based system (FRBS), an adaptive Mamdani fuzzy modeling (AMFM) is introduced in this paper. It differs from the original Mamdani FRBS in that it applies different membership functions and a defuzzification mechanism that is ‘differentiable’ with respect to the membership function parameters. The proposed system also includes a back error propagation (BEP) algorithm that is used to refine the fuzzy model. The efficacy of the proposed AMFM approach is demonstrated through the experimental trails from a compressor in an industrial gas turbine system

Research of monitoring and measuring equipment using the Fuzzy-Logic system

В роботі розглянуто вирішення науково-практичної задачі контролю точності вимірювання параметрів технологічного процесу виготовлення карамелі для підвищення її якості за допомогою створення евристичного аналізатору на базі інтерфейсу користувача системи Fuzzy-Logic. Проаналізовано фактори, що впливають на точність вимірювання, доведено можливість застосування апарату Fuzzy-Logic для визначення таких параметрів технологічного процесу, які забезпечують максимальну якість продукції. Проведено комп’ютерне моделювання, яке підтвердило, що створення евристичного аналізатору для визначення якості карамелі доцільно та необхідно для того, щоб не допустити виробництво неякісної продукції. На підставі даних, отриманих з результатів натурних вимірювань параметрів технологічного процесу виготовлення карамельного сиропу проведено розрахунки стандартної невизначеності результатів вимірювань по типам А та В, щоб мати можливість своєчасно прогнозувати відмову датчиків на основі зміни форми закону розподілу результатів вимірювань та назначати міжповірочні інтервали для досліджуваного обладнання.The paper considers the solution of the scientific and practical problem of controlling the accuracy of measuring the parameters of the technological process of making caramel to improve its quality by creating a heuristic analyzer based on the interface of the Fuzzy-Logic system. The factors affecting the measurement accuracy are analyzed, the possibility of using the Fuzzy-logic apparatus to determine such process parameters that ensure maximum product quality is proved. Computer simulation was carried out, which confirmed that the creation of a heuristic analyzer to determine the quality of caramel is appropriate and necessary in order to prevent the production of low-quality products. Based on the data obtained from the results of field measurements of the parameters of the technological process of making caramel syrup, the standard uncertainty of the measurement results for types A and B was calculated in order to be able to timely predict sensor failures based on the change in the shape of the distribution law of the measurement results and determine the calibration interval for the equipment under study

Variational Bayesian modified adaptive Mamdani Fuzzy Modelling for use in condition monitoring

The paper proposes a new Adaptive Mamdani Fuzzy Model (AMFM) based system modelling methodology that improves on traditional Mamdani fuzzy rule based system (FRBS) techniques through use of alternative membership functions and a defuzzification mechanism that is ‘differentiable’, allowing a back error propagation (BEP) algorithm to refine the initial fuzzy model. Moreover, a variational Bayesian (VB) method is applied to simplify the results via automatic selection of the number of input rules so that redundant rules can be removed for the initial modelling phase. The efficacy of the proposed VB modified AMFM (VB-AMFM) approach is demonstrated through experimental trials using measurements from a compressor in an industrial gas turbine (IGT)

Start-up vibration analysis for novelty detection on industrial gas turbines

This paper focuses on industrial application of start-up vibration signature analysis for novelty detection with experimental trials on industrial gas turbines (IGTs). Firstly, a representative vibration signature is extracted from healthy start-up vibration measurements through the use of an adaptive neuro-fuzzy inference system (ANFIS). Then, the first critical speed and the vibration level at the critical speed are located from the signature. Finally, two (s- and v-) health indices are introduced to detect and identify different novel/fault conditions from the IGT start-ups, in addition to traditional similarity measures, such as Euclidean distance and cross-correlation measures. Through a case study on IGTs, it is shown that the presented approach provides a convenient and efficient tool for IGT condition monitoring using start-up field data

Implementation of Mamdani Fuzzy Method in Employee Promotion System

Nowadays, employees are big assets to an institution. Every employee has a different educational background, degree, work skill, attitude and ethic that affect the performance. An institution including government institution implements a promotion system in order to improve the performance of the employees. Pangandaran Tourism, Industry, Trade, and SME Department is one of government agency that implements a promotion system to discover employees who deserve to get promotion. However, there are some practical deficiencies in the promotion system, one of which is the subjectivity issue. This work proposed a classification model that could minimize the subjectivity issue in employee promotion system. This paper reported a classification employee based on their eligibility for promotion. The degree of membership was decided using Mamdani Fuzzy based on determinant factors of the performance of employees. In the evaluation phase, this model had an accuracy of 91.4%. It goes to show that this model may minimize the subjectivity issue in the promotion system, especially at Pangandaran Tourism, Industry, Trade, and SME Departmen

Multi-region System Modelling by using Genetic Programming to Extract Rule Consequent Functions in a TSK Fuzzy System

[EN] This paper aims to build a fuzzy system by means of genetic programming, which is used to extract the relevant function for each rule consequent through symbolic regression. The employed TSK fuzzy system is complemented with a variational Bayesian Gaussian mixture clustering method, which identifies the domain partition, simultaneously specifying the number of rules as well as the parameters in the fuzzy sets. The genetic programming approach is accompanied with an orthogonal least square algorithm, to extract robust rule consequent functions for the fuzzy system. The proposed model is validated with a synthetic surface, and then with real data from a gas turbine compressor map case, which is compared with an adaptive neuro-fuzzy inference system model. The results have demonstrated the efficacy of the proposed approach for modelling system with small data or bifurcating dynamics, where the analytical equations are not available, such as those in a typical industrial setting.Research supported by EPSRC Grant EVES (EP/R029741/1).Zhang, Y.; Martínez-García, M.; Serrano, J.; Latimer, A. (2019). Multi-region System Modelling by using Genetic Programming to Extract Rule Consequent Functions in a TSK Fuzzy System. IEEE. 987-992. https://doi.org/10.1109/ICARM.2019.8834163S98799

Estimating gas turbine compressor discharge temperature using Bayesian neuro-fuzzy modelling

The objective of this paper is to estimate the compressor discharge temperature measurements on an industrial gas turbine that is undergoing commissioning at site, using a data-driven model which is built using the test bed measurements of the engine. This paper proposes a Bayesian neuro-fuzzy modelling (BNFM) approach, which combines the adaptive neuro-fuzzy inference system (ANFIS) and variational Bayesian Gaussian mixture model (VBGMM) techniques. A data-driven compressor model is built using ANFIS, and VBGMM is applied in the set-up stage to automatically select the number of input membership functions in the fuzzy system. The efficacy of the proposed BFNM approach is established through experimental trials of a sub-15MW gas turbine, and the results, from the model that is built using test bed data, are shown to be promising for estimating the compressor discharge temperatures on the gas turbine during commissioning

A Knowledge Transfer Platform for Fault Diagnosis of Industrial Gas Turbines

The aim of this paper is to introduce the bases of an intelligent fault diagnostic platform, which assists in detecting mechanical failures of Industrial Gas Turbines (IGTs). This comprises an integration of an expert system and its complementary signal processing techniques. The essential characteristic here is not to exclude humans (experts) from the diagnostic process, but rather to transfer their knowledge and experience to a computerized platform. The automated process executed by the computerized platform is to ensure the scalability and consistency in fault diagnosis; while the humans are required to corroborate the transparency and liability of the outcomes. In this paper, a Knowledge Transfer Platform (KTP) is proposed for fault diagnosis of industrial systems. It is then designed and tested for combustion fault diagnosis using field data of IGTs. The preliminary results have revealed the feasibility and efficacy of the proposed scheme, which has the potential to be further extended to a large industrial scale and to different engineering diagnostic applications

A thermodynamic transient model for performance analysis of a twin shaft Industrial Gas Turbine

In this study, a Simulink model based on fundamental thermodynamic principles to predict the dynamic and steady state performance in a twin shaft Industrial Gas Turbine (IGT) has been developed. The components comprising the IGT have been implemented in the modelling architecture using a thermodynamic commercial toolbox (Thermolib, EUtech Scientific Engineering GmbH) and Simulink environment. Measured air pressure and air temperature discharged by compressor allowed the validation of the modelling architecture. The model assisted the development of a computational tool based on Artificial Neural Network (ANN) for compressor fault diagnostics in IGTs. It has been demonstrated that modelling plays an important role to predict and monitor gas turbine system performance at different operating and ambient conditions