Integration of a failure monitoring within a hybrid dynamic simulation environment

The complexity and the size of the industrial chemical processes induce the monitoring of a growing number of process variables. Their knowledge is generally based on the measurements of system variables and on the physico-chemical models of the process. Nevertheless this information is imprecise because of process and measurement noise. So the research ways aim at developing new and more powerful techniques for the detection of process fault. In this work, we present a method for the fault detection based on the comparison between the real system and the reference model evolution generated by the extended Kalman filter. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. It is a general object-oriented environment which provides common and reusable components designed for the development and the management of dynamic simulation of industrial systems. The use of this method is illustrated through a didactic example relating to the field of Chemical Process System Engineering

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Modeling and fault tolerant control of an electro-hydraulic actuator

In the modern industry, electro-hydraulic actuators (EHAs) have been applied to various applications for precise position pressure/ force control tasks. However, operating EHAs under sensor faults is one of the critical challenges for the control engineers. For its enormous nonlinear characteristics, sensor fault could lead the catastrophic failure to the overall system or even put human life in danger. Thus in this paper, a study on mathematical modeling and fault tolerant control (FTC) of a typical EHA for tracking control under sensor-fault conditions has been carried out. In the proposed FTC system, the extended Kalman-Bucy unknown input observer (EKBUIO) -based robust sensor fault detection and identification (FDI) module estimates the system states and the time domain fault information. Once a fault is detected, the controller feedback is switched from the faulty sensor to the estimated output from the EKBUIO owing to mask the sensor fault swiftly and retains the system stability. Additionally, considering the tracking accuracy of the EHA system, an efficient brain emotional learning based intelligent controller (BELBIC) is suggested as the main control unit. Effectiveness of the proposed FTC architecture has been investigated by experimenting on a test bed using an EHA in sensor failure conditions

Condition Monitoring of Pneumatic Drive Systems Based on the AI Method Feed-Forward Backpropagation Neural Network

Machine condition monitoring is used in a variety of industries as a very efficient strategy for equipment maintenance. This paper presents a study on monitoring a pneumatic system using a feed-forward backpropagation neural network as a classifier and compares the results obtained with different sensor signals and associated extracted features as input for classification. The vibrations of the body of a pneumatic cylinder are acquired using both common industrial sensors and low-cost sensors integrated into an Arduino board. Pressure sensors for both chambers and a position sensor are also used. Power spectral density (PSD) is used to extract features from the acceleration signals, as well as statistical indices. Statistical indices are considered for pressure and position sensors. The results, which are based on experimental data obtained on a test bench, show that a feed-forward neural network makes it possible to identify the operating states with a good degree of reliability. Even with low-cost instrumentation, it is possible to realize reliable condition monitoring based on vibrations. This last result is particularly important as it can help to further increase the uptake of this maintenance approach in the industrial environment

Design and development of prognostic and health management system for fly-by-wire primary flight control

Electro-Hydraulic Servo Actuators (EHSA) is the principal technology used for primary flight control in new aircrafts and legacy platforms. The development of Prognostic and Health Management technologies and their application to EHSA systems is of great interest in both the aerospace industry and the air fleet operators. This Ph.D. thesis is the results of research activity focused on the development of a PHM system for servovalve of fly-by-wire primary flight EHSA. One of the key features of the research is the implementation of a PHM system without the addition of new sensors, taking advantage of sensing and information already available. This choice allows extending the PHM capability to the EHSAs of legacy platforms and not only to new aircrafts. The enabling technologies borrow from the area of Bayesian estimation theory and specifically particle filtering and the information acquired from EHSA during pre-flight check is processed by appropriate algorithms in order to obtain relevant features, detect the degradation and estimate the Remaining Useful Life (RUL). The results are evaluated through appropriate metrics in order to assess the performance and effectiveness of the implemented PHM system. The major objective of this contribution is to develop an innovative fault diagnosis and failure prognosis framework for critical aircraft components that integrates effectively mathematically rigorous and validated signal processing, feature extraction, diagnostic and prognostic algorithms with novel uncertainty representation and management tools in a platform that is computationally efficient and ready to be transitioned on-board an aircraft

Performance Evaluation of a Prognostic Framework for Electro-Hydraulic Actuators for Stability Control Augmentation Systems with Different Sensors Suites

Stability Control Augmentation Systems (SCAS) are widely adopted to enhance the flight stability of rotary-wing aircraft operating in difficult aerodynamic conditions, such as low altitude missions, stationary flight nearby vertical walls or in presence of heavy gusts. Such systems are based upon small electro-hydraulic servosystems controlled in position through a dedicated servovalve. The SCAS operates with limited authority over the main control linkage translating the pilot input in the movement of the main flight control actuator. Being critical for the operability of the helicopter, the definition of a Prognostics and Health Management (PHM) framework for the SCAS systems would provide significant advantages, such as better risk mitigation, improved availability, and a reduction in the occurrences of unpredicted failures which still represent one of the most known downsides of helicopters due to their very severe operational environment. Since SCAS actuators are usually equipped with a low number of sensors, it is at the present time unclear whether a fully realized PHM system can be prepared without resorting to the introduction of additional sensors. This paper deals with this subject evaluating the performances of a fault diagnosis tool operating considering different sensors suite (traditional and with additional sensors), and different PHM strategies, using in-flight data or their combination with dedicated pre-flight checks to cover the most common failure modes. The analysis is then completed with an evaluation of the prognostic capabilities of the proposed strategies, highlighting benefits and limitations of the proposed solutions

Incipient fault detection and isolation of sensors and field devices

The purpose of this research is to develop a robust fault detection and isolation method, for detecting faults in process sensors, actuators, controllers and other field devices. The approach to the solution to this problem is summarized below. A novel approach for the validation of control system components and sensors was developed in this research. The process is composed of detecting a system anomaly, isolating the faulty component (such as sensors, actuators, and controllers), computing its deviation from expected value for a given system\u27s normal condition, and finally reconstructing its output when applicable. A variant of the Group Method of Data Handling (GMDH) was developed in this research for generating analytical redundancy from relationships among different system components. A rational function approximation was used for the data-driven modeling scheme. This analytical redundancy is necessary for detecting system anomalies and isolating faulty components. A rule-base expert system was developed in order to isolate the faulty component. The rule-based was established from model-simulated data. A fuzzy-logic estimator was implemented to compute the magnitude of the loop component fault so that the operator or the controller might take corrective actions. This latter engine allows the system to be operated in a normal condition until the next scheduled shutdown, even if a critical component were detected as degrading. The effectiveness of the method developed in this research was demonstrated through simulation and by implementation to an experimental control loop. The test loop consisted of a level control system, flow, pressure, level and temperature measuring sensors, motor-operated valves, and a pump. Commonly observed device faults were imposed in different system components such as pressure transmitters, pumps, and motor-operated valves. This research has resulted in a framework for system component failure detection and isolation, allowing easy implementation of this method in any process control system (power plants, chemical industry, and other manufacturing industry). The technique would also aid the plant personnel in defining the minimal number of sensors to be installed in a process system, necessary for reliable component validation

Computational intelligence techniques for HVAC systems: a review

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air conditioning (HVAC) systems are the major source of energy consumption in buildings and an ideal candidate for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems.The analysis of trends reveals the minimization of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE–2, HVACSim+ and ESP–r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on MAS, as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions

Design and Development of a Decision Support System for Safety Management of Rotary Pump Systems

Increasing technological advancement and complexity have made it necessary to develop more effective approaches to safety, reliability and quality. This paper presents the design and development of decision support system for safety management of rotary system using computational intelligent techniques. The rotary system considered for this research paper is centrifugal pumping system. This paper presents the application of Neural Network approach for fault detection and fuzzy logic approach for fault diagnosis in centrifugal pumping system. This paper highlights the development of decision support system integrating all the subsystem for a real-world application of computational intelligent techniques to solve a complex problem, which contributes to the prevention of accidents and preparation for emergency response. The results are compared and the conclusions are presented which demonstrate the possible application of industrial use