Hybrid Random Forest and Support Vector Machine Modeling for HVAC Fault Detection and Diagnosis

The malfunctioning of the heating, ventilating, and air conditioning (HVAC) system is considered to be one of the main challenges in modern buildings. Due to the complexity of the building management system (BMS) with operational data input from a large number of sensors used in HVAC system, the faults can be very difficult to detect in the early stage. While numerous fault detection and diagnosis (FDD) methods with the use of statistical modeling and machine learning have revealed prominent results in recent years, early detection remains a challenging task since many current approaches are unfeasible for diagnosing some HVAC faults and have accuracy performance issues. In view of this, this study presents a novel hybrid FDD approach by combining random forest (RF) and support vector machine (SVM) classifiers for the application of FDD for the HVAC system. Experimental results demonstrate that our proposed hybrid random forest–support vector machine (HRF–SVM) outperforms other methods with higher prediction accuracy (98%), despite that the fault symptoms were insignificant. Furthermore, the proposed framework can reduce the significant number of sensors required and work well with the small number of faulty training data samples available in real-world applications.

Squirrel cage induction motor fault diagnosis using lissajous curve of an auxiliary winding voltage: case of broken bars

The detection of incipient faults has attracted industrials and researchers specific attention in order to prevent the motor breakdown, improve its reability and increase its lifetime. This paper presents a squirrel cage induction machine broken bar and rings diagnosis approach. This technic uses a new monitored signal as an auxiliary winding voltage related to a small coil inserted between two stator phases. Monitoring behaviors of the Lissajous curve of this auxiliary winding voltage park components under different load levels is the main key of this study. For this purpose, the squirrel cage induction machine modeling and the explicit expressions developed for the inserted winding voltage and its Park components will be presented. Then, an induction machine with different broken cases: one broken bar, two broken bars, broken end ring and broken bars with end ring are investigated. The simulation results confirm the validity of the proposed approach.

The Walking Robot Control System that is Adaptive to Changes in the Kinematics

The walking mobile robots (WMR) have recently become widely popular in robotics. They are especially useful in the extreme cases: search and rescue operations; cargo delivery over highly rough terrain; building a map. These robots also serve to explore and describe a partially or completely non-deterministic workspace, as well as to explore areas that are dangerous to human life. One of the main requirements for these WMR is the robustness of its control system. It allows WMR to maintain the operability when the characteristics of the support surface change as well as under more severe conditions, in particular, loss of controllability or damage of the supporting limb (SL). We propose to use the principles of genetic programming to create a WMR control system that allows a robot to adapt to possible changes in its kinematics, as well as to the characteristics of the support surface on which it moves. This approach does not require strong computational power or a strict formal classification of possible damage to the WMR. This article discusses two main WMR control modes: standard, which accord to a serviceable kinematics, and emergency, in which one or more SL drives are damaged or lost controllability. As an example, the structure of the control system of the WMP is proposed, the kinematics of which is partially destroyed in the process of movement. We developed a method for controlling such robot, which is based on the use of a genetic algorithm in conjunction with the Mealy machine. Modeling of modes of movement of WMR with six SL was carried out in the V-REP program for two cases of injury: absent and not functioning limb. We present the results of simulation of emergency gaits for these configurations of WMP and the effectiveness of the proposed method in the case of damage to the kinematic scheme. We also compared the performance of the genetic algorithm for the damaged WMR with the standard control algorithm.

Involvement of the habenula in the pathophysiology of autism spectrum disorder

The habenula is a small epithalamic structure with widespread connections to multiple cortical, subcortical and brainstem regions. It has been identified as the central structure modulating the reward value of social interactions, behavioral adaptation, sensory integration and circadian rhythm. Autism spectrum disorder (ASD) is characterized by social communication deficits, restricted interests, repetitive behaviors, and is frequently associated with altered sensory perception and mood and sleep disorders. The habenula is implicated in all these behaviors and results of preclinical studies suggest a possible involvement of the habenula in the pathophysiology of this disorder. Using anatomical magnetic resonance imaging and automated segmentation we show that the habenula is significantly enlarged in ASD subjects compared to controls across the entire age range studied (6–30 years). No differences were observed between sexes. Furthermore, support-vector machine modeling classified ASD with 85% accuracy (model using habenula volume, age and sex) and 64% accuracy in cross validation. The Social Responsiveness Scale (SRS) significantly differed between groups, however, it was not related to individual habenula volume. The present study is the first to provide evidence in human subjects of an involvement of the habenula in the pathophysiology of ASD.

Intelligent human-machine modeling for enhancing remote learning environments

This thesis describes the design and testing of a videoconferencing system for supporting the academic and social needs of hospitalized high school students. The underlying technologies of PEBBLES (Providing Education by Bringing Learning Environments to Students) were incorporated into the High School PEBBLES Prototype (HSPP) with new functionality such as application sharing and a whiteboard. Laboratory studies were conducted with four groups of high school students in a simulated classroom/hospital environment, assigning them a storyboarding task that encouraged use of the prototype's videoconferencing and application sharing features. The results indicated that the students could work collaboratively through the HSPP, and the students were able to experience presence. Some of the critical requirements for effective presence through videoconferencing were identified. The systems development approach used in this thesis highlights the value of intelligent modeling of systems in order to meet the specific requirements of the users.

Intelligent human-machine modeling for enhancing remote learning environments

This thesis describes the design and testing of a videoconferencing system for supporting the academic and social needs of hospitalized high school students. The underlying technologies of PEBBLES (Providing Education by Bringing Learning Environments to Students) were incorporated into the High School PEBBLES Prototype (HSPP) with new functionality such as application sharing and a whiteboard. Laboratory studies were conducted with four groups of high school students in a simulated classroom/hospital environment, assigning them a storyboarding task that encouraged use of the prototype's videoconferencing and application sharing features. The results indicated that the students could work collaboratively through the HSPP, and the students were able to experience presence. Some of the critical requirements for effective presence through videoconferencing were identified. The systems development approach used in this thesis highlights the value of intelligent modeling of systems in order to meet the specific requirements of the users.

Stirling Refrigerating Machine Modeling Using Schmidt and Finite Physical Dimensions Thermodynamic Models: A Comparison with Experiments

The purpose of the study is to show that two simple models that take into account only the irreversibility due to temperature difference in the heat exchangers and imperfect regeneration are able to indicate refrigerating machine behavior. In the present paper, the finite physical dimensions thermodynamics (FPDT) method and 0-D modeling using the Schmidt model with imperfect regeneration were applied in the study of a β type Stirling refrigeration machine.The 0-D modeling is improved by including the irreversibility caused by imperfect regeneration and the finite temperature difference between the gas and the heat exchangers wall. A flowchart of the Stirling refrigerator exergy balance is presented to show the internal and external irreversibilities. It is found that the irreversibility at the regenerator level is more important than that at the heat exchangers level. The energies exchanged by the working gas are expressed according to the practical parameters, necessary for the engineer during the entire project. The results of the two thermodynamic models are presented in comparison with the experimental results, which leads to validation of the proposed FPDT model for the functional and constructive parameters of the studied refrigerating machine.