Intelligent fault detection and diagnosis based o optimized fuzzy model for process control rig

This thesis focuses on the application of artificial intelligent techniques in fault detection and diagnosis. Fault detection and diagnosis scheme is a technique used in supervisory systems. The function of the supervisory system is to indicate unnecessary process states and to take the most appropriate actions to maintain continuous operation and to avoid damages. There are two main methods in fault detection and diagnosis: model free and model-based. In this thesis, model-based fault detection and diagnosis is used. One of the research challenges in model-based fault detection and diagnosis of a system is to find the accurate models. The objective of this thesis is to detect and diagnose the faults to a process control rig. A technique for the modeling of nonlinear control processes using fuzzy modeling approach based on the Takagi–Sugeno fuzzy model with a combination of genetic algorithm and recursive least square is proposed. This thesis discusses the identification of the parameters at the antecedent and consequent parts of the fuzzy model. For the antecedent fuzzy parameters, genetic algorithm is used to tune them while at the consequent part, recursive least squares approach is used to identify the system parameters. The proposed method is used to develop fault model and to detect the fault where this task is performed by using residual signals. When the residual signal is zero or nearly zero, the system is in normal condition, and when the fault occurs, residual signals should distinctively diverge from zero. Meanwhile, neural network is used for fault classification where this task is performed by identifying the fault in the system. This approach is applied to a process control rig with three subsystems: a heating element, a heat exchanger and a compartment tank. Experimental results show that the proposed approach provides better modeling when compared with Takagi Sugeno fuzzy modeling technique and the linear modeling approach. The overall accuracy for classification results also shows the best performance of around 93%

DEVELOPMENT AND IMPLEMENTATION OF ADVANCED PROCESS CONTROLLERS (APC) FOR FLOW APPLICATION

The main objective of this project is to improve the capability of conventional PID controller for flow control by developing and implementing Advanced Process Controllers (APCs) which consist of Fuzzy Logic Controller (FLC) and Adaptive Neuro-Fuzzy Inference System (ANFIS). Flow is one of the most difficult process variables (PV) to control because the properties of a fluid can easily change. This can lead the system to become non-linear process dynamic. When disturbances present, flow control will become more difficult to control and the conventional PID controller will not be the best option. As a solution, in this project APCs will be developed and the controllers‟ performance will be compare with the conventional PID controller. Development of the controllers will be based on MATLAB/Simulink Toolboxes. Then, the designed controllers will be implemented onto the PcA SimExpert Mobile Pilot Plant SE231B-21 - Flow Control and Calibration Process Unit. In addition, for having better maintenance and data trend collection for the process, Human Machine Interface (HMI) will be developed by using MATLAB/Simulink. In designing the controllers, it will involve data gathering, designing and tuning membership functions and develop rule base. Based on the experiment results, APCs show better control performance as compared to PID controller

Neuro-fuzzy software for intelligent control and education

Tese de mestrado integrado. Engenharia Electrotécnica e de Computadores (Major Automação). Faculdade de Engenharia. Universidade do Porto. 200

Automated On-line Fault Prognosis for Wind Turbine Monitoring using SCADA data

Current wind turbine (WT) studies focus on improving their reliability and reducing the cost of energy, particularly when WTs are operated offshore. A Supervisory Control and Data Acquisition (SCADA) system is a standard installation on larger WTs, monitoring all major WT sub-assemblies and providing important information. Ideally, a WT’s health condition or state of the components can be deduced through rigorous analysis of SCADA data. Several programmes have been made for that purpose; however, the resulting cost savings are limited because of the data complexity and relatively low number of failures that can be easily detected in early stages. This thesis develops an automated on-line fault prognosis system for WT monitoring using SCADA data, concentrating particularly on WT pitch system, which is known to be fault significant. A number of preliminary activities were carried out in this research. They included building a dedicated server, developing a data visualisation tool, reviewing the existing WT monitoring techniques and investigating the possible AI techniques along with some examples detailing applications of how they can be utilised in this research. The a-priori knowledge-based Adaptive Neuro-Fuzzy Inference System (APK-ANFIS) was selected to research in further because it has been shown to be interpretable and allows domain knowledge to be incorporated. A fault prognosis system using APK-ANFIS based on four critical WT pitch system features is proposed. The proposed approach has been applied to the pitch data of two different designs of 26 Alstom and 22 Mitsubishi WTs, with two different types of SCADA system, demonstrating the adaptability of APK-ANFIS for application to variety of technologies. After that, the Alstom results were compared to a prior general alarm approach to show the advantage of prognostic horizon. In addition, both results are evaluated using Confusion Matrix analysis and a comparison study of the two tests to draw conclusions, demonstrating that the proposed approach is effective

Soft sensor development and process control of anaerobic digestion

This thesis focuses on soft sensor development based on fuzzy logic used for real time online monitoring of anaerobic digestion to improve methane output and for robust fermentation. Important process parameter indicators such as pH, biogas production, daily difference in pH and daily difference in biogas production were used to infer alkalinity, a reliable indicator of process stability. Additionally, a fuzzy logic and a rule-based controller were developed and tested with single stage anaerobic digesters operating with cow slurry and cellulose. Alkalinity predictions from the fuzzy logic algorithm were used by both controllers to regulate the organic loading rate that aimed to optimise the biogas process. The predictive performance of a software sensor determining alkalinity that was designed using fuzzy logic and subtractive clustering and was validated against multiple linear regression models that were developed (Partner N° 2, Rothamsted Research 2010) for the same purpose. More accurate alkalinity predictions were achieved by utilizing a fuzzy software sensor designed with less amount of data compared to a multiple linear regression model whose design was based on a larger database. Those models were utilised to control the organic loading rate of a twostage, semi-continuously fed stirred reactor system. Three 5l reactors without support media and three 5l reactors with different support media (burst cell reticulated polyurethane foam coarse, burst cell reticulated polyurethane foam medium and sponge) were operated with cow slurry for a period of seven weeks and twenty weeks respectively. Reactors with support media were proven to be more stable than the reactors without support media but did not exhibit higher gas productivity. Biomass support media were found to influence digester recovery positively by reducing the recovery period. Optimum process parameter ranges were identified for reactors with and without support media. Increased biogas production was found to occur when the loading rates were 3-3.5g VS/l/d and 4-5g VS/l/d respectively. Optimum pH ranges were identified between 7.1-7.3 and 6.9-7.2 for reactors with and without support media respectively, whereas all reactors became unstable at ph<6.9. Alkalinity levels for system stability appeared to be above 3500 mg/l of HCO3 - for reactors without media and 3480 mg/l of HCO3 - for reactors with support media. Biogas production was maximized when alkalinity was 3 between 3500-4500 mg/l of HCO3 - for reactors without support media and 3480- 4300 mg/l of HCO3 - for reactors with support media. Two fuzzy logic models predicting alkalinity based on the operation of the three 5l reactors with support media were developed (FIS I, FIS II). The FIS II design was based on a larger database than FIS I. FIS II performance when applied to the reactor where sponge was used as the support media was characterized by quite good MAE and bias values of 466.53 mg/l of HCO3- and an acceptable value for R2= 0.498. The NMSE was close to 0 with a value of 0.03 and a slightly higher FB= 0.154 than desired. The fuzzy system robustness was tested by adding NaHCO3 to the reactor with the burst cell reticulated polyurethane foam medium and by diluting the reactor where sponge was used as the support media with water. FIS I and FIS II were able to follow the system output closely in the first case, but not in the second. FIS II functionality as an alkalinity predictor was tested through the application on a 28l cylindrical reactor with sponge as the biomass support media treating cow manure. If data that was recorded when severe temperature fluctuations occurred (that highly impact digester performance), are excluded, FIS II performance can be characterized as good by having R2= 0.54 and MAE=Bias= 587 mg/l of HCO3-. Predicted alkalinity values followed observed alkalinity values closely during the days that followed NaHCO3 addition and water dilution. In a second experiment a rulebased and a Mamdani fuzzy logic controller were developed to regulate the organic loading rate based on alkalinity predictions from FIS II. They were tested through the operation of five 6.5l reactors with biomass support media treating cellulose. The performance indices of MAE=763.57 mg/l of HCO3-, Bias= 398.39 mg/l of HCO3-, R2= 0.38 and IA= 0.73 indicate a pretty good correlation between predicted and observed values. However, although both controllers managed to keep alkalinity within the desired levels suggested for stability (>3480 mg/l of HCO3-), the reactors did not reach a stable state suggesting that different loading rates should be applied for biogas systems treating cellulose.New Generation Biogas (NGB

ANFIS Based Data Rate Prediction For Cognitive Radio

Intelligence is needed to keep up with the rapid evolution of wireless communications, especially in terms of managing and allocating the scarce, radio spectrum in the highly varying and disparate modern environments. Cognitive radio systems promise to handle this situation by utilizing intelligent software packages that enrich their transceiver with radio-awareness, adaptability and capability to learn. A cognitive radio system participates in a continuous process, the ‘‘cognition cycle”, during which it adjusts its operating parameters, observes the results and, eventually takes actions, that is to say, decides to operate in a specific radio configuration (i.e., radio access technology, carrier frequency, modulation type, etc.) expecting to move the radio toward some optimized operational state. In such a process, learning mechanisms utilize information from measurements sensed from the environment, gathered experience and stored knowledge and guide in decision making. This thesis introduces and evaluates learning schemes that are based on adaptive neuro-fuzzy inference system (ANFIS) for predicting the capabilities (e.g. data rate) that can be achieved by a specific radio configuration in cognitive radio. First a ANFIS based scheme is proposed. The work reported here is compare previous neural network based learning schemes. Cognitive radio is a intelligent emergent technology, where learning schemes are needed to assist in its functioning. ANFIS based scheme is one of the good learning Artificial intelligence method, that combines best features of neural network and fuzzy logic. Here ANFIS and neural networks methods are able to assist a cognitive radio system to help in selecting the best one radio configuration to operate in. Performance metric like RMSE, prediction accuracy of ANFIS learning has been used as performance index

Novel Tornado-Like Vortex Generator with Intelligent Controller

Cooking fumes may cause multiple adverse health effects, and range hoods play central roles in controlling indoor air pollution caused by cooking fumes. However, the traditional design of the range hoods has a low efficiency due to its working principle, and the efficiency decreases rapidly as the mounting height of the exhaust hood increases. This thesis is aimed at design and building a novel tornado-like vortex generator (TLVG) with an intelligent controller to enhance the efficiency of traditional range hood. Both experimental results and numerical simulation indicate that most of the cooking fumes are spreading to surrounding areas when the traditional range hood is working alone, while the cooking fumes are drawn into the tornado-like vortex and exhausted through the range hood when the novel TLVG is on. The effects of various factors on the efficiency of sucking cooking fumes are analyzed by orthogonal experiment design. The results show that the key factor affecting the performance of the TLVG is the horizontal jet angle. A higher jet velocity results in a lower negative pressure, which helps concentrate and exhaust the fume. The results also reveal that the exhaust flow velocity marginally affects the pressure around the source of cooking fumes, but the tornado-like vortex cannot be produced when the value of the exhaust flow velocity is too high. In addition, the figures of the velocity field, pressure field, and tracking particle field are plotted and analyzed. In this thesis, an intelligent controller of TLVG is designed and simulated to adapt to various types of range hoods. Adaptive-Network-based Fuzzy Inference System (ANFIS) is used in this intelligent controller, which combines the merits of both Fuzzy Inference Systems and Neural Networks. The results from the numerical simulation of the TLVG can be used to train and test the neural fuzzy system. Besides, Particle Swarm Optimization (PSO) is used for effective training in ANFIS networks. Digital simulation results demonstrate that the designed ANFIS-Swarm controller realizes a better prediction of the checking data than that from a basic ANFIS controller. This study provides information for improving the kitchen environment, and it can also be applied to different types of range hood, exhaust ventilation system, and air pollution control

Experimental investigation and modelling of the heating value and elemental composition of biomass through artificial intelligence

Abstract: Knowledge advancement in artificial intelligence and blockchain technologies provides new potential predictive reliability for biomass energy value chain. However, for the prediction approach against experimental methodology, the prediction accuracy is expected to be high in order to develop a high fidelity and robust software which can serve as a tool in the decision making process. The global standards related to classification methods and energetic properties of biomass are still evolving given different observation and results which have been reported in the literature. Apart from these, there is a need for a holistic understanding of the effect of particle sizes and geospatial factors on the physicochemical properties of biomass to increase the uptake of bioenergy. Therefore, this research carried out an experimental investigation of some selected bioresources and also develops high-fidelity models built on artificial intelligence capability to accurately classify the biomass feedstocks, predict the main elemental composition (Carbon, Hydrogen, and Oxygen) on dry basis and the Heating value in (MJ/kg) of biomass...Ph.D. (Mechanical Engineering Science

Fault Mode Probability Factor Based Fault-Tolerant Control for Dissimilar Redundant Actuation System

This paper presents a Fault Mode Probability Factor (FMPF) based Fault-Tolerant Control (FTC) strategy for multiple faults of Dissimilar Redundant Actuation System (DRAS) composed of Hydraulic Actuator (HA) and Electro-Hydrostatic Actuator (EHA). The long-term service and severe working conditions can result in multiple gradual faults which can ultimately degrade the system performance, resulting in the system model drift into the fault state characterized with parameter uncertainty. The paper proposes to address this problem by using the historical statistics of the multiple gradual faults and the proposed FMPF to amend the system model with parameter uncertainty. To balance the system model precision and computation time, a Moving Window (MW) method is used to determine the applied historical statistics. The FMPF based FTC strategy is developed for the amended system model where the system estimation and Linear Quadratic Regulator (LQR) are updated at the end of system sampling period. The simulations of DRAS system subjected to multiple faults have been performed and the results indicate the effectiveness of the proposed approach