The thesis work details the design and testing of a wireless electrocardiogram (ECG) system. This system includes a wireless ECG device, as well as software packages to visually display the waveform locally on a computer and remotely on a web page. The remote viewing capability also extends to using an Android phone application. The purpose of the system is to serve as a means for a doctor or physician to check up on a patient away from a hospital setting. This system allows for a patient to be in their home environment while giving health vital information, primarily being the heart’s activity through the ECG, to medical personnel.

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / A monitoring system has been developed to detect when a large vehicle is gaining access to an area such as an agricultural field or facility through a control gate. The system uses multiple sensors, including Hall-effect, anisotropic magnetoresistor, ultrasonic ranging, and vision. A user is alerted using a conventional cell phone network of the presence of the vehicle. The system is microcontroller based, uses photovoltaic power supply, and leverages commercial off the shelf components wherever feasible. The system detection algorithm was made adaptable, to minimize false alarms and missed detections.

The design and implementation of LabVIEW-aided power system SCADA (Supervisory Control And Data Acquisition) for industrial applications is presented in this thesis. The system includes some sensors¡Bcontrol hardware, and two fore-microprocessors; and it runs in the environment of popular windows by personal computer, using LabVIEW between human and machine. The mainframe computer can get the electrical power parameters (such as voltage¡Bcurrent and power factor )from the fore-microprocessor via RS-485 communication interface. All of the power parameters and control signals are transmitted upon the network, so it can also command the remote controllers to detect the status and control the switching gear of the remote equipments. These data can be recorded and stored simultaneously in the LabVIEW environment and displayed on the screen. The design accomplishes function of signal acquisition and data transmission features low cost¡Bhigh stability, with remote controller, and easy expansion. It can mange and control the conventional household or industrial electric equipment, to achieve the goal of energy conservation.

ITC/USA 2014 Conference Proceedings / The Fiftieth Annual International Telemetering Conference and Technical Exhibition / October 20-23, 2014 / Town and Country Resort & Convention Center, San Diego, CA / This paper describes a device that integrates remote monitoring and control electronics into a commercial off the shelf 120 VAC power distribution strip and surge protector. An integrated microcontroller collects data on power usage from each of four AC outlets, along with two USB ports, and relays this information to a remote location. Using a conventional web browser to generate a graphical user interface, an untrained user can easily visualize their current and past energy usage patterns, and send commands to control individual outlets.

Fault detection and diagnosis is quite important in engineering systems, and deserves further attention in view of the increasing complexity of modern machinery. Traditional single-sensor methods of fault monitoring and diagnosis may find it difficult to meet modern industrial requirements because there is usually no direct way to measure and accurately correlate a machine fault to a single sensor output. Fusion of information from multiple sensors can overcome this shortcoming. In this thesis, a neural-fuzzy approach of multi-sensor fusion is developed for a network-enabled remote fault diagnosis system. The approach is validated by applying it to an industrial machine called the Iron Butcher, which is a machine used in the fish processing industry for the removal of the head in fish prior to further processing for canning. An important characteristic of the fault diagnosis approach developed in this thesis is to make an accurate decision of the machine condition by fusing information from different sensors. First, sound, vibration and vision signals are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Second, the sound and vibration signals are transformed into the frequency domain using fast Fourier transformation (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. Third, Sound, vibration and vision feature vectors are provided as inputs to a neuro-fuzzy network for fault detection and diagnosis. A four-layer neural network including a fuzzy hidden layer is developed in the thesis to analyze and diagnose existing faults. By training the neural network with sample data for typical faults, faults of five crucial components in the fish cutting machine are detected with high reliability and robustness. Alarms to warn about impending faults may be generated as well during the machine operation. A network-based remote monitoring architecture is developed as well in the thesis, which will facilitate engineers to monitor the machine condition in a more flexible manner from a remote site. Developed multi-sensor approaches are validated using computer simulations and physical experimentation with the industrial machine, and compared with a single-sensor approach.

Fault detection and diagnosis is quite important in engineering systems, and deserves further attention in view of the increasing complexity of modern machinery. Traditional single-sensor methods of fault monitoring and diagnosis may find it difficult to meet modern industrial requirements because there is usually no direct way to measure and accurately correlate a machine fault to a single sensor output. Fusion of information from multiple sensors can overcome this shortcoming. In this thesis, a neural-fuzzy approach of multi-sensor fusion is developed for a network-enabled remote fault diagnosis system. The approach is validated by applying it to an industrial machine called the Iron Butcher, which is a machine used in the fish processing industry for the removal of the head in fish prior to further processing for canning. An important characteristic of the fault diagnosis approach developed in this thesis is to make an accurate decision of the machine condition by fusing information from different sensors. First, sound, vibration and vision signals are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Second, the sound and vibration signals are transformed into the frequency domain using fast Fourier transformation (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. Third, Sound, vibration and vision feature vectors are provided as inputs to a neuro-fuzzy network for fault detection and diagnosis. A four-layer neural network including a fuzzy hidden layer is developed in the thesis to analyze and diagnose existing faults. By training the neural network with sample data for typical faults, faults of five crucial components in the fish cutting machine are detected with high reliability and robustness. Alarms to warn about impending faults may be generated as well during the machine operation. A network-based remote monitoring architecture is developed as well in the thesis, which will facilitate engineers to monitor the machine condition in a more flexible manner from a remote site. Developed multi-sensor approaches are validated using computer simulations and physical experimentation with the industrial machine, and compared with a single-sensor approach.

Fault detection and diagnosis is quite important in engineering systems, and deserves further attention in view of the increasing complexity of modern machinery. Traditional single-sensor methods of fault monitoring and diagnosis may find it difficult to meet modern industrial requirements because there is usually no direct way to measure and accurately correlate a machine fault to a single sensor output. Fusion of information from multiple sensors can overcome this shortcoming. In this thesis, a neural-fuzzy approach of multi-sensor fusion is developed for a network-enabled remote fault diagnosis system. The approach is validated by applying it to an industrial machine called the Iron Butcher, which is a machine used in the fish processing industry for the removal of the head in fish prior to further processing for canning. An important characteristic of the fault diagnosis approach developed in this thesis is to make an accurate decision of the machine condition by fusing information from different sensors. First, sound, vibration and vision signals are acquired from the machine using a microphone, an accelerometer and a digital CCD camera, respectively. Second, the sound and vibration signals are transformed into the frequency domain using fast Fourier transformation (FFT). A feature vector from the FFT frequency spectra is defined and extracted from the acquired information. Also, a feature based vision tracking approach—the Scale Invariant Feature Transform (SIFT)—is applied to the vision data to track the object of interest (fish) in a robust manner. Third, Sound, vibration and vision feature vectors are provided as inputs to a neuro-fuzzy network for fault detection and diagnosis. A four-layer neural network including a fuzzy hidden layer is developed in the thesis to analyze and diagnose existing faults. By training the neural network with sample data for typical faults, faults of five crucial components in the fish cutting machine are detected with high reliability and robustness. Alarms to warn about impending faults may be generated as well during the machine operation. A network-based remote monitoring architecture is developed as well in the thesis, which will facilitate engineers to monitor the machine condition in a more flexible manner from a remote site. Developed multi-sensor approaches are validated using computer simulations and physical experimentation with the industrial machine, and compared with a single-sensor approach. / Applied Science, Faculty of / Mechanical Engineering, Department of / Graduate

Includes abstract. / Includes bibliographical references (p. 127-129). / Faults and inefficiencies in air conditioning installations account for between 2% and 11% of allenergy consumed by commercial buildings in the United States each year. Diagnostics systems havebeen proven to improve the performance of air conditioning plants but the high costs of purchasing,retrofitting and maintaining such a system results in limited market adoption of such systems.This thesis discusses the design, implementation and results of low-cost remote monitoring anddiagnostic platform for use in air conditioning installations. The design of the various hardwarecomponents is presented along with the structure of the framework developed for each device. The thesis also contains information regarding the selection, integration and installation of the various types of sensors required on the various installations. A specially-designed protocol was also developed to handle communication between the hardware devices. Both the physical configuration and details of the protocol structure are presented in detail in this thesis. The mechanism through which the device uploads data to a server is also described in this thesis and includes details on both the hardware and the server technologies used in the upload process. The system has been installed on two different sites in Cape Town, South Africa and has produced meaningful diagnostic information since November 2007.

Heart failure (HF) is the leading cause of cardiovascular morbidity and health care utilization inCanada. Much of the cost for HF is related to hospitalization, strategies to decrease cost need tofocus on avoiding unnecessary readmissions to the hospital. Interactive voice response (IVR) is anautomated telephony system that leverages existing telephone lines to monitor patients post-discharge from a hospital, for early intervention. Limited evidence exists on the pattern of use andsuccess of IVR technology among patients with heart failure and how IVR impacts theircompliance. This study explores the pattern of IVR use by HF patients in the IVR program at theUniversity of Ottawa Heart Institute (UOHI), describes their characteristics and IVR patterns ofuse in relation to occurrence of symptoms, compliance behavior (e.g., weighing themselves,medication compliance) and service utilization (i.e., hospital readmission). The system is based onan algorithm that triggers automated telephone calls to patients at a predetermined time for 3months after discharge. A total of 902 HF patients were considered with a mean age of 70 years(59.4% male). Over the 12 weeks, results showed an overall increase in medication adherence anda decrease in symptom occurrence, weight gain and readmission rates. The highest compliancerate in this study was found in medication adherence and the lowest was found in the variableassociated with exercise. The risk of readmission for patients who completed the IVR call,answered all the questions and listened to the educational prompts was lower than the patients whowere called back by nurses. These results suggest that IVR calls do have a positive impact on HFpatients. The increased use of IVR in remote patient monitoring will allow for a cheaper and moreaccessible form of at home monitoring. Leveraging IVR technology to support other conditions,especially during a pandemic, may be beneficial for patients to avoid unnecessary visits to thehospital and complications due to delay in seeking care.

A simulation model analyzes cost effectiveness of remote facility monitoring for risk reduction in rural water supply systems by performing a break-even analysis that compares operating costs with manual and remote monitoring. Water system operating cost includes the value of water loss (i.e., realized risk) resulting from operating excursions which are inversely related to mechanical reliability. Reliability is controlled by facility monitoring that identifies excursions enabling operators to implement mitigating measures. Cost effectiveness refers to the cost relationship among operating alternatives that reveals changed economic conditions at different operating rates inherent in the inverse relationship between fixed and variable costs. Break-even analysis describes cost effectiveness by identifying the operating rate above which the more capital intensive alternative will result in lower operating cost. Evidence indicates that increased monitoring frequency associated with remote monitoring can reduce water system operating cost by improving reliability, but whether remote monitoring is cost effective depends upon system-specific factors. The lack of a documented tool for evaluating this type of cost effectiveness led to the project objective of developing a model that performs break-even analysis by simulating water system operating costs as functions of system size (delivery rate). When the spreadsheet-based static deterministic parametric simulation model is run for the Tazewell County, Virginia water system based upon 1998 data, break even is predicted at approximately fifty-five percent of annual capacity (116,338,000 gallons) with operating cost of $1,043,400. Maximum annual operating cost reduction from a $317,600 investment provides payback in nine years. / Master of Science