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Permanent memory fault analysis, faults in combinatorial networks, linear machines, and path sensitizing in network analysis for reliable spacecraft data systems
Modern buildings are being equipped with increasingly sophisticated power and control systems with substantial capabilities for monitoring and controlling the amenities. Operational problems associated with heating, ventilation, and air-conditioning (HVAC) systems plague many commercial buildings, often the result of degraded equipment, failed sensors, improper installation, poor maintenance, and improperly implemented controls. Most existing HVAC fault-diagnostic schemes are based on analytical models and knowledge bases. These schemes are adequate for generic systems. However, real-world systems significantly differ from the generic ones and necessitate modifications of the models and/or customization of the standard knowledge bases, which can be labor intensive. Data-driven techniques for fault detection and isolation (FDI) have a close relationship with pattern recognition, wherein one seeks to categorize the input-output data into normal or faulty classes. Owing to the simplicity and adaptability, customization of a data-driven FDI approach does not require in-depth knowledge of the HVAC system. It enables the building system operators to improve energy efficiency and maintain the desired comfort level at a reduced cost. In this article, we consider a data-driven approach for FDI of chillers in HVAC systems. To diagnose the faults of interest in the chiller, we employ multiway dynamic principal component analysis (MPCA), multiway partial least squares (MPLS), and support vector machines (SVMs). The simulation of a chiller under various fault conditions is conducted using a standard chiller simulator from the American Society of Heating, Refrigerating, and Air-conditioning Engineers (ASHRAE). We validated our FDI scheme using experimental data obtained from different types of chiller faults.
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A feasibility study to investigate the application of vibration monitoring to the rotating machinery of planned NASA advanced spacecraft components is described. Factors investigated include: (1) special problems associated with small, high RPM machines; (2) application across multiple component types; (3) microgravity; (4) multiple fault types; (5) eight different analysis techniques including signature analysis, high frequency demodulation, cepstrum, clustering, amplitude analysis, and pattern recognition are compared; and (6) small sample statistical analysis is used to compare performance by computation of probability of detection and false alarm for an ensemble of repeated baseline and faulted tests. Both detection and classification performance are quantified. Vibration monitoring is shown to be an effective means of detecting the most important problem types for small, high RPM fans and pumps typical of those planned for the advanced spacecraft. A preliminary monitoring system design and implementation plan is presented.
There is a growing call among researchers for tighter coupling between human factors and human reliability assessments. In this research, we explore if early design stage resilience assessments can help bridge some of the gaps between human factors and human reliability assessments. Resilience in systems is their ability to recover reasonably and operate within acceptable bounds during failures and unexpected events. The fmdtools toolkit allows designers to assess the resilience of a system by modeling the human error and machine-related failure propagation in both nominal and faulty scenarios during the early design stages. As a result, the fmdtools toolkit has a low-fidelity dynamic human reliability assessment component built into it. In this paper, we study if the results from the fmdtools simulations can help inform and prioritize human factors design decision-making, resulting in tighter coupling between human factors and human reliability assessments during the design process. Specifically, we explore the results from a rover design example to understand the types of information that can help guide human factor-related decision-making.
Transformation of data into knowledge through conceptual induction has been the focus of our research described in this paper. We have developed a Machine Learning System (MLS) to analyze the rocket engine simulation data. MLS can provide to its users fault analysis, characteristics, and conceptual descriptions of faults, and the relationships of attributes and sensors. All the results are critically important in identifying faults.
Potential applications for autonomous space platforms (SP) are discussed. The platforms are assumed to have long in-service lifetimes and therefore be flexible as to configuration modification and payload changeout. Higher degrees of autonomy, particularly from ground control, are made possible because of the rapid increase of microprocessor power and artificial intelligence advances. Functioning independently, the platforms are to rely only on periodic refurbishment visits by, e.g., the Orbiter. The Manned Space Station (MSS) will be the most complex structure, involving multifacted man-machine interfaces. The SP can be subsystems of the MSS (or other platforms), handling communications enunciation, data acquisition, analysis and telemetry, fault detection and isolation, systems monitoring and control, etc. The SP adopted will depend in all cases on costs vs benefits analyses to determine the worth of removing the function(s) from direct, regular human intervention.
A practical methodology for the study of fault and error latency is demonstrated under a real workload. This is the first study that measures and quantifies the latency under real workload and fills a major gap in the current understanding of workload-failure relationships. The methodology is based on low level data gathered on a VAX 11/780 during the normal workload conditions of the installation. Fault occurrence is simulated on the data, and the error generation and discovery process is reconstructed to determine latency. The analysis proceeds to combine the low level activity data with high level machine performance data to yield a better understanding of the phenomena. A strong relationship exists between latency and workload and that relationship is quantified. The sampling and reconstruction techniques used are also validated. Error latency in the memory where the operating system resides was studied using data on the physical memory access. Fault latency in the paged section of memory was determined using data from physical memory scans. Error latency in the microcontrol store was studied using data on the microcode access and usage.
Mathematical models capable of simulating the transient, steady state, and faulted performance characteristics of various brushless dc machine-PSA (power switching assembly) configurations were developed. These systems are intended for possible future use as primemovers in EMAs (electromechanical actuators) for flight control applications. These machine-PSA configurations include wye, delta, and open-delta connected systems. The research performed under this contract was initially broken down into the following six tasks: development of mathematical models for various machine-PSA configurations; experimental validation of the model for failure modes; experimental validation of the mathematical model for shorted turn-failure modes; tradeoff study; and documentation of results and methodology.
Design guidelines and functional systems being considered in the process of defining the configuration of the automated systems for a manned space station are outlined. The requirements are dependent on life-cycle costing and will set the necessary level of automation, as well as autonomy from outside commands. Fault protection routines have been largely devised according to successful programming on the Voyager spacecraft. An analysis is still needed of the housekeeping functions, including human necessities, machine functions, and mission objectives. A data base will result, defining the functions that have historically been delegated to either man or machine. Care must be taken to coordinate and document stationkeeping functions that might interface with mission functions. A data management system that is flexible with regards to changing mission objectives and to the MTBF factors, which will determine the level of technology to be used is required. Expert systems will be integrated into the automation to guide the machines in problem solving, including ensuring adequate management of the battery subsystem.
Spacecraft processors and memory are subjected to high radiation doses and therefore employ radiation-hardened components. However, these components are orders of magnitude more expensive than typical desktop components, and they lag years behind in terms of speed and size. We have integrated algorithm-based fault tolerance (ABFT) methods into onboard data analysis algorithms to detect radiation-induced errors, which ultimately may permit the use of spacecraft memory that need not be fully hardened, reducing cost and increasing capability at the same time. We have also developed a lightweight software radiation simulator, BITFLIPS, that permits evaluation of error detection strategies in a controlled fashion, including the specification of the radiation rate and selective exposure of individual data structures. Using BITFLIPS, we evaluated our error detection methods when using a support vector machine to analyze data collected by the Mars Odyssey spacecraft. We found ABFT error detection for matrix multiplication is very successful, while error detection for Gaussian kernel computation still has room for improvement.
The purpose of this research is focussed on the identification/demonstration of critical technology innovations that will be applied to various applications viz. Detection of automated machine Health Monitoring (BM, real-time data analysis and control of Systems Under Test (SUT). This new innovation using a High Fidelity Dynamic Model-based Simulation (BFDMS) approach will be used to implement a real-time monitoring, Test and Evaluation (T&E) methodology including the transient behavior of the system under test. The unique element of this process control technique is the use of high fidelity, computer generated dynamic models to replicate the behavior of actual Systems Under Test (SUT). It will provide a dynamic simulation capability that becomes the reference truth model, from which comparisons are made with the actual raw/conditioned data from the test elements.
Schneider generalizes a number of protocols for Byzantine fault tolerant clock synchronization and presents a uniform proof for their correctness. The authors present a machine checked proof of this schematic protocol that revises some of the details in Schneider's original analysis. The verification was carried out with the EHDM system developed at the SRI Computer Science Laboratory. The mechanically checked proofs include the verification that the egocentric mean function used in Lamport and Melliar-Smith's Interactive Convergence Algorithm satisfies the requirements of Schneider's protocol.
The analysis of human monitoring and decision making behavior as well as its modeling are described. Classic and optimal control theoretical, monitoring models are surveyed. The relationship between attention allocation and eye movements is discussed. As an example of applications, the evaluation of predictor displays by means of the optimal control model is explained. Fault detection involving continuous signals and decision making behavior of a human operator engaged in fault diagnosis during different operation and maintenance situations are illustrated. Computer aided decision making is considered as a queueing problem. It is shown to what extent computer aids can be based on the state of human activity as measured by psychophysiological quantities. Finally, management information systems for different application areas are mentioned. The possibilities of mathematical modeling of human behavior in complex man machine systems are also critically assessed.
Version 3 of the NPARC Navier-Stokes code includes support for large-grain (block level) parallelism using explicit message passing between a heterogeneous collection of computers. This capability has the potential for significant performance gains, depending upon the block data distribution. The parallel implementation uses a master/worker arrangement of processes. The master process assigns blocks to workers, controls worker actions, and provides remote file access for the workers. The processes communicate via explicit message passing using an interface library which provides portability to a number of message passing libraries, such as PVM (Parallel Virtual Machine). A Bourne shell script is used to simplify the task of selecting hosts, starting processes, retrieving remote files, and terminating a computation. This script also provides a simple form of fault tolerance. An analysis of the computational performance of NPARC is presented, using data sets from an F/A-18 inlet study and a Rocket Based Combined Cycle Engine analysis. Parallel speedup and overall computational efficiency were obtained for various NPARC run parameters on a cluster of IBM RS6000 workstations. The data show that although NPARC performance compares favorably with the estimated potential parallelism, typical data sets used with previous versions of NPARC will often need to be reblocked for optimum parallel performance. In one of the cases studied, reblocking increased peak parallel speedup from 3.2 to 11.8.
Context: Software developers spend a significant amount of time fixing faults. However, not many papers have addressed the actual effort needed to fix software faults. Objective: The objective of this paper is twofold: (1) analysis of the effort needed to fix software faults and how it was affected by several factors and (2) prediction of the level of fix implementation effort based on the information provided in software change requests. Method: The work is based on data related to 1200 failures, extracted from the change tracking system of a large NASA mission. The analysis includes descriptive and inferential statistics. Predictions are made using three supervised machine learning algorithms and three sampling techniques aimed at addressing the imbalanced data problem. Results: Our results show that (1) 83% of the total fix implementation effort was associated with only 20% of failures. (2) Both safety critical failures and post-release failures required three times more effort to fix compared to non-critical and pre-release counterparts, respectively. (3) Failures with fixes spread across multiple components or across multiple types of software artifacts required more effort. The spread across artifacts was more costly than spread across components. (4) Surprisingly, some types of faults associated with later life-cycle activities did not require significant effort. (5) The level of fix implementation effort was predicted with 73% overall accuracy using the original, imbalanced data. Using oversampling techniques improved the overall accuracy up to 77%. More importantly, oversampling significantly improved the prediction of the high level effort, from 31% to around 85%. Conclusions: This paper shows the importance of tying software failures to changes made to fix all associated faults, in one or more software components and/or in one or more software artifacts, and the benefit of studying how the spread of faults and other factors affect the fix implementation effort.
The design methodologies of using sequence diagrams, multi-process functional flow diagrams, and hierarchical state machines were successfully applied in designing three MSL (Mars Science Laboratory) flight software modules responsible for handling actuator motions of the CHIMRA (Collection and Handling for In Situ Martian Rock Analysis), IC (Inlet Covers), and DRT (Dust Removal Tool) mechanisms. The methodologies were essential to specify complex interactions with other modules, support concurrent foreground and background motions, and handle various fault protections. Studying task scenarios with multi-process functional flow diagrams yielded great insight to overall design perspectives. Since the three modules require three different levels of background motion support, the methodologies presented in this paper provide an excellent comparison. All three modules are fully operational in flight.
In order to obtain a measure of its responses to short circuits a large horizontal axis wind turbine generator was modeled and its performance was simulated on a digital computer. Simulation of short circuit faults on the synchronous alternator of a wind turbine generator, without resort to the classical assumptions generally made for that analysis, indicates that maximum clearing times for the system tied to an infinite bus are longer than the typical clearing times for equivalent capacity conventional machines. Also, maximum clearing times are independent of tower shadow and wind shear. Variation of circuit conditions produce the modifications in the transient response predicted by analysis.