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

Machine Performance and Condition Monitoring Using Motor Operating Parameters Through Artificial Intelligence Techniques

Condition monitoring (CM) of gearboxes is a necessary activity due to the crucial importance of gearboxes in power transmission in most industrial applications. There has long been pressure to improve measuring techniques and develop analytical tools for early fault detection in gearboxes. This thesis develops new gearbox monitoring methods by demonstrating that operating parameters (static data) obtained from machine control processes can be used, rather than parameters obtained from vibration and acoustic measurements. Such a development has important implications for the future of CM techniques because it could greatly simplify the measurement process. To monitor the gearbox under different operating and fault conditions based on the static data, three artificial intelligence (AI) techniques: a general regression neural network (GRNN), a back propagation neural network (BPNN), and an adaptive neuro-fuzzy inference system (ANFIS) have been used successfully to capture nonlinear variations of the electric motor current and control parameters such as load settings and temperatures. The three AI systems are taught the expected values of current; load and temperature for the gearbox in a given condition, and then measured values obtained from the gearbox with a known fault introduced are assessed by each of the AI models to indicate the presence of this abnormal condition. The experimental results show that each of GRNN, BPNN and ANFIS are adequate and are able to serve as an effective tool for gearbox condition monitoring and fault detection. The main contributions of this study is to examine the performance of a model based condition monitoring approach by using just operating parameters for fault detection in a two stage gearbox. A model for current prediction is developed using an ANFIS, GRNN and BPNN which captures the complicated inter-relations between measured variables, and uses direct comparison between the measured and predicted values for fault detection

Fault tolerant strategy for actively controlled railway wheelset

Traditionally, solid axle railway wheelsets are stabilised by using passive suspensions on a conventional rail vehicle, but such additional stiffness affects the pure rolling action of the wheelset around the curve. It has been theoretically proven that this design conflict between stability and curving performance can be solved by applying active control instead of conventional passive components, resulting in the reduction of the wear of the wheels and track by minimising the track shifting forces. In the active approach, the use of actuators, sensors and data processors to replace the traditional passive suspension raises the issue of the system safety in the event of a failure of the active control, which could result in the loss of stability and in more severe cases, derailment. Further on, in active control systems for railway vehicles the actuators tend to be significantly more expensive and require more additional space than sensors, and an electronic control unit. Therefore, developing an analytical redundancy-based fault tolerance technique for an actively controlled wheelset that minimises the number of actuators will clearly be more beneficial. Thus the emphasis of this research is to develop a fault-tolerant system of active control for a railway vehicle in the event of actuator malfunction in order to guarantee stability and good curving performance without using additional actuators. The key achievements of this research can be summarised as follows: •The research considers three of the most common types of actuator failure for the electro-mechanical actuators: fail-hard (FH), short circuit (SC) and open circuit (OC). The fail-hard is a failure condition when the motor shaft of the actuator becomes immovable, whereas the short circuit and open circuit are failures that occur in the electrical parts of the actuator which correspond to zero voltage and zero current in the motor respectively.•The research investigates and develops a thorough understanding of the effect of actuator faults and failure modes on the vehicle behaviour that provides the necessary foundation for the development of the proposed fault-tolerant strategy.•An effective fault detection and isolation methods for actuator faults through two different approaches is developed; the vehicle model-based approach and the actuator model-based approach. Additionally, the research takes into account the reliability and robustness of the FDI schemes in the presence of sensor failures and parameter uncertainties in the system. •The research develops the control re-configuration in order to cope with the identified failure mode of the actuator in order to maintain the vehicle stability and desired curving performance