Framework for a space shuttle main engine health monitoring system

A framework developed for a health management system (HMS) which is directed at improving the safety of operation of the Space Shuttle Main Engine (SSME) is summarized. An emphasis was placed on near term technology through requirements to use existing SSME instrumentation and to demonstrate the HMS during SSME ground tests within five years. The HMS framework was developed through an analysis of SSME failure modes, fault detection algorithms, sensor technologies, and hardware architectures. A key feature of the HMS framework design is that a clear path from the ground test system to a flight HMS was maintained. Fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and demonstrated on data from SSME ground test failures. The fault detection algorithms exhibited 100 percent detection of faults, had an extremely low false alarm rate, and were robust to sensor loss. These algorithms were incorporated into a hierarchical decision making strategy for overall assessment of SSME health. A preliminary design for a hardware architecture capable of supporting real time operation of the HMS functions was developed. Utilizing modular, commercial off-the-shelf components produced a reliable low cost design with the flexibility to incorporate advances in algorithm and sensor technology as they become available

Recent Advances in Pipeline Monitoring and Oil Leakage Detection Technologies: Principles and Approaches

Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed. Document type: Articl

Recent advances in pipeline monitoring and oil leakage detection technologies: principles and approaches.

Pipelines are widely used for the transportation of hydrocarbon fluids over millions of miles all over the world. The structures of the pipelines are designed to withstand several environmental loading conditions to ensure safe and reliable distribution from point of production to the shore or distribution depot. However, leaks in pipeline networks are one of the major causes of innumerable losses in pipeline operators and nature. Incidents of pipeline failure can result in serious ecological disasters, human casualties and financial loss. In order to avoid such menace and maintain safe and reliable pipeline infrastructure, substantial research efforts have been devoted to implementing pipeline leak detection and localisation using different approaches. This paper discusses pipeline leakage detection technologies and summarises the state-of-the-art achievements. Different leakage detection and localisation in pipeline systems are reviewed and their strengths and weaknesses are highlighted. Comparative performance analysis is performed to provide a guide in determining which leak detection method is appropriate for particular operating settings. In addition, research gaps and open issues for development of reliable pipeline leakage detection systems are discussed

Decision support with data-analysis methods in a nuclear power plant

Early fault detection is an important issue in nuclear industry. Methods based on self-organizing map (SOM) in dynamic systems are discussed and developed to help operators and plant experts in their decision making and used together with other methods. Visualization issues are in an important role in this research. Prototype systems are built to be able to test the basic principles. Five different studies are presented in detail. This report summarizes the test case 4 (TC4) "Decision support at a nuclear power plant" in NoTeS and NoTeS2 projects in TEKES MASI research program

Liquid Transport Pipeline Monitoring Architecture Based on State Estimators for Leak Detection and Location

This research presents the implementation of optimization algorithms to build auxiliary signals that can be injected as inputs into a pipeline in order to estimate —by using state observers—physical parameters such as the friction or the velocity of sound in the fluid. For the state estimator design, the parameters to be estimated are incorporated into the state vector of a Liénard-type model of a pipeline such that the observer is constructed from the augmented model. A prescribed observability degree of the augmented model is guaranteed by optimization algorithms by building an optimal input for the identification. The minimization of the input energy is used to define the optimality of the input, whereas the observability Gramian is used to verify the observability. Besides optimization algorithms, a novel method, based on a Liénard-type model, to diagnose single and sequential leaks in pipelines is proposed. In this case, the Liénard-type model that describes the fluid behavior in a pipeline is given only in terms of the flow rate. This method was conceived to be applied in pipelines solely instrumented with flowmeters or in conjunction with pressure sensors that are temporarily out of service. The design approach starts with the discretization of the Liénard-type model spatial domain into a prescribed number of sections. Such discretization is performed to obtain a lumped model capable of providing a solution (an internal flow rate) for every section. From this lumped model, a set of algebraic equations (known as residuals) are deduced as the difference between the internal discrete flows and the nominal flow (the mean of the flow rate calculated prior to the leak). The residual closest to zero will indicate the section where a leak is occurring. The main contribution of our method is that it only requires flow measurements at the pipeline ends, which leads to cost reductions. Some simulation-based tes

Microcontroller-based transient signal analysis and distributed system for intelligent process monitoring

The research presented in this thesis considers the feasibility of utilising dsPICs (digital signal controllers) in the development of effective monitoring systems which have the capability to adapt to changes in operating conditions and can be quickly calibrated to suit a range of applications, thus helping to reduce the development time constraint. The capability of these monitoring solutions to detect and isolate faults occurring in pneumatic processes is investigated and their effectiveness verified. Three applications are considered gas pipe leakage, linear actuator operations and gripper action. In each case, solutions are developed based upon the dsPIC. The solutions utilise the analysis of pressure transients to overcome the limitation in the dsPIC memory. The deployment of minimal sensors and electronics was essential to optimise the cost of the system. Leak detection techniques are developed with application to gas fitting pipes. The speed at which correct decisions are determined was the essence of this work. The solutions are tested, compared and their capability validated using pipes which had been rejected according to industrial standards. In this application a dsPIC digital signal controller and a pressure sensor were deployed, thus ensuring a low cost monitoring solution. Linear actuator "end of stroke" monitoring has, previously, largely been possible using limit switches. A more challenging method based upon the deployment of a pressure sensor is outlined. Monitoring model surfaces were obtained and their capability to determine the health of the process was proved, at various supply pressures. With regard to the gripper monitoring, a performance surface by which the gripper action can be monitored is generated and embedded within the dsPIC. Various faults are simulated and their effect on the gripper performance investigated. Leakage and blockage are also investigated at various places in the pneumatic circuit to allow for an algorithm to be devised. Faults may be detected and isolated, and their locations identified to allow for timely recovery treatment, thus supporting an enhanced process monitoring strategy.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Design and Implementation of Smart Sensors with Capabilities of Process Fault Detection and Variable Prediction

A typical sensor consists of a sensing element and a transmitter. The major functions of a transmitter are limited to data acquisition and communication. The recently developed transmitters with ‘smart’ functions have been focused on easy setup/maintenance of the transmitter itself such as self-calibration and self-configuration. Recognizing the growing computational capabilities of microcontroller units (MCUs) used in these transmitters and underutilized computational resources, this thesis investigates the feasibility of adding additional functionalities to a transmitter to make it ‘smart’ without modifying its foot-print, nor adding supplementary hardware. Hence, a smart sensor is defined as sensing elements combined with a smart transmitter. The added functionalities enhance a smart sensor with respect to performing process fault detection and variable prediction. This thesis starts with literature review to identify the state-of-the-arts in this field and also determine potential industry needs for the added functionalities. Particular attentions have been paid to an existing commercial temperature transmitter named NCS-TT105 from Microcyber Corporation. Detailed examination has been made in its internal hardware architecture, software execution environment, and additional computational resources available for accommodating additional functions. Furthermore, the schemes of the algorithms for realizing process fault detection and variable prediction have been examined from both theoretical and feasibility perspectives to incorporate onboard NCS-TT105. An important body of the thesis is to implement additional functions in the MCUs of NCS-TT105 by allocating real-time execution of different tasks with assigned priorities in the real-time operating system (RTOS). The enhanced NCS-TT105 has gone through extensive evaluation on a physical process control test facility under various normal/fault conditions. The test results are satisfactory and design specifications have been achieved. To the best knowledge of the author, this is the first time that process fault detection and variable prediction have been implemented right onboard of a commercial transmitter. The enhanced smart transmitter is capable of providing the information of incipient faults in the process and future changes of critical process variables. It is believed that this is an initial step towards the realization of distributed intelligence in process control, where important decisions regarding the process can be made at a sensor level

Small business innovation research. Abstracts of 1988 phase 1 awards

Non-proprietary proposal abstracts of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA are presented. Projects in the fields of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robots, computer sciences, information systems, data processing, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

Microcontroller-based transient signal analysis and distributed system for intelligent process monitoring

The research presented in this thesis considers the feasibility of utilising dsPICs (digital signal controllers) in the development of effective monitoring systems which have the capability to adapt to changes in operating conditions and can be quickly calibrated to suit a range of applications, thus helping to reduce the development time constraint. The capability of these monitoring solutions to detect and isolate faults occurring in pneumatic processes is investigated and their effectiveness verified. Three applications are considered gas pipe leakage, linear actuator operations and gripper action. In each case, solutions are developed based upon the dsPIC. The solutions utilise the analysis of pressure transients to overcome the limitation in the dsPIC memory. The deployment of minimal sensors and electronics was essential to optimise the cost of the system. Leak detection techniques are developed with application to gas fitting pipes. The speed at which correct decisions are determined was the essence of this work. The solutions are tested, compared and their capability validated using pipes which had been rejected according to industrial standards. In this application a dsPIC digital signal controller and a pressure sensor were deployed, thus ensuring a low cost monitoring solution. Linear actuator 'end of stroke' monitoring has, previously, largely been possible using limit switches. A more challenging method based upon the deployment of a pressure sensor is outlined. Monitoring model surfaces were obtained and their capability to determine the health of the process was proved, at various supply pressures. With regard to the gripper monitoring, a performance surface by which the gripper action can be monitored is generated and embedded within the dsPIC. Various faults are simulated and their effect on the gripper performance investigated. Leakage and blockage are also investigated at various places in the pneumatic circuit to allow for an algorithm to be devised. Faults may be detected and isolated, and their locations identified to allow for timely recovery treatment, thus supporting an enhanced process monitoring strategy