Distributed Self Fault Diagnosis in Wireless Sensor Networks using Statistical Methods

Wireless sensor networks (WSNs) are widely used in various real life applications where the sensor nodes are randomly deployed in hostile, human inaccessible and adversarial environments. One major research focus in wireless sensor networks in the past decades has been to diagnose the sensor nodes to identify their fault status. This helps to provide continuous service of the network despite the occurrence of failure due to environmental conditions. Some of the burning issues related to fault diagnosis in wireless sensor networks have been addressed in this thesis mainly focusing on improvement of diagnostic accuracy, reduction of communication overhead and latency, and robustness to erroneous data by using statistical methods. All the proposed algorithms are evaluated analytically and implemented in standard network simulator NS3 (version 3.19). A distributed self fault diagnosis algorithm using neighbor coordination (DSFDNC) is proposed to identify both hard and soft faulty sensor nodes in wireless sensor networks. The algorithm is distributed (runs in each sensor node), self diagnosable (each node identifies its fault status) and can diagnose the most common faults like stuck at zero, stuck at one, random data and hard faults. In this algorithm, each sensor node gathered the observed data from the neighbors and computes the mean to check the presence of faulty sensor node. If a node diagnoses a faulty sensor node in the neighbors, then it compares observed data with the data of the neighbors and predicts its probable fault status. The final fault status is determined by diffusing the fault information obtained from the neighbors. The accuracy and completeness of the algorithm are verified based on the statistical analysis over sensors data. The performance parameters such as diagnosis accuracy, false alarm rate, false positive rate, total number of message exchanges, energy consumption, network life time, and diagnosis latency of the DSFDNC algorithm are determined for different fault probabilities and average degrees and compared with existing distributed fault diagnosis algorithms. To enhance the diagnosis accuracy, another self fault diagnosis algorithm is proposed based on hypothesis testing (DSFDHT) using the neighbor coordination approach. The Newman-Pearson hypothesis test is used to diagnose the soft fault status of each sensor node along with the neighbors. The algorithm can diagnose the faulty sensor node when the average degree of the network is less. The diagnosis accuracy, false alarm rate and false positive rate performance of the DSFDHT algorithm are improved over DSFDNC for sparse wireless sensor networks by keeping other performance parameters nearly same. The classical methods for fault finding using mean, median, majority voting and hypothesis testing are not suitable for large scale wireless sensor networks due to large devi- ation in transmitted data by faulty sensor nodes. Therefore, a modified three sigma edit test based self fault diagnosis algorithm (DSFD3SET) is proposed which diagnoses in an efficient manner over a large scale wireless sensor networks. The diagnosis accuracy, false alarm rate, and false positive rate of the proposed algorithm improve as compared to that of the DSFDNC and DSFDHT algorithms. The algorithm enhances the total number of message exchanges, energy consumption, network life time, and diagnosis latency, because the proposed algorithm needs less number of message exchanges over the algorithms such as DSFDNC and DSFDHT. In the DSFDNC, DSFDHT and DSFD3SET algorithms, the faulty sensor nodes are considered as soft faulty nodes which behave permanently. However in wireless sensor networks, the sensor nodes behave either fault free or faulty during different periods of time and are considered as intermittent faulty sensor nodes. Diagnosing intermittent faulty sensor nodes in wireless sensor networks is a challenging problem, because of inconsistent result patterns generated by the sensor nodes. The traditional distributed fault diagnosis (DIFD) algorithms consume more message exchanges to obtain the global fault status of the network. To optimize the number of message exchanges over the network, a self fault diagnosis algorithm is proposed here, which repeatedly conducts the self fault diagnosis procedure based on the modified three sigma edit test over a duration to identify the intermittent faulty sensor nodes. The algorithm needs less number of iterations to identify the intermittent faulty sensor nodes. The simulation results show that, the performance of the HISFD3SET algorithm improves in diagnosis accuracy, false alarm rate and false positive rate over the DIFD algorith

Similarity Matching Techniques For Fault Diagnosis In Automotive Infotainment Electronics

Fault diagnosis has become a very important area of research during the last decade due to the advancement of mechanical and electrical systems in industries. The automobile is a crucial field where fault diagnosis is given a special attention. Due to the increasing complexity and newly added features in vehicles, a comprehensive study has to be performed in order to achieve an appropriate diagnosis model. A diagnosis system is capable of identifying the faults of a system by investigating the observable effects (or symptoms). The system categorizes the fault into a diagnosis class and identifies a probable cause based on the supplied fault symptoms. Fault categorization and identification are done using similarity matching techniques. The development of diagnosis classes is done by making use of previous experience, knowledge or information within an application area. The necessary information used may come from several sources of knowledge, such as from system analysis. In this paper similarity matching techniques for fault diagnosis in automotive infotainment applications are discussed

Knowledge representation into Ada parallel processing

The Knowledge Representation into Ada Parallel Processing project is a joint NASA and Air Force funded project to demonstrate the execution of intelligent systems in Ada on the Charles Stark Draper Laboratory fault-tolerant parallel processor (FTPP). Two applications were demonstrated - a portion of the adaptive tactical navigator and a real time controller. Both systems are implemented as Activation Framework Objects on the Activation Framework intelligent scheduling mechanism developed by Worcester Polytechnic Institute. The implementations, results of performance analyses showing speedup due to parallelism and initial efficiency improvements are detailed and further areas for performance improvements are suggested

A model for a space shuttle safing and failure-detection expert

The safing and failure-detection expert (SAFE) is a prototype for a malfunction detection, diagnosis, and safing system for the atmospheric revitalization subsystem (ARS) in the Space Shuttle orbiter. SAFE, whose knowledge was extracted from expert-provided heuristics and documented procedures, automatically manages all phases of failure handling: detection, diagnosis, testing procedures, and recovery instructions. The SAFE architecture allows it to handle correctly sensor failures and multiple malfunctions. Since SAFE is highly interactive, it was used as a test bed for the evaluation of various advanced human-computer interface (HCI) techniques. The use of such expert systems in the next generation of space vehicles would increase their reliability and autonomy to levels not achievable before

Comparing verbal media for alarm handling: Speech versus textual displays

The rise of computers in command and control domains has meant that control operations can be performed via desk-based visual display terminals. This trend has also produced the potential to display information to operators in a variety of formats. Of particular interest has been the use of text-based displays for alarm presentation. There are possible limitations to the use of text for alarm presentation, not least of which is the need for a dedicated alarms display screen (or, at least, a display page). Given the capability of computers to synthesize speech, it is possible that speech-based alarms could generate the same information as text-based displays without the need for dedicated screen space. In this paper an experimental comparison of speech-based and text-based displays for presentation of alarms is reported. The findings show that speech leads to longer response times than text displays, but that it has minimal effect on the efficacy of fault handling. The results are discussed within the alarm initiated activities framework and implications for alarm system design are outlined

Planning and Resource Management in an Intelligent Automated Power Management System

Power system management is a process of guiding a power system towards the objective of continuous supply of electrical power to a set of loads. Spacecraft power system management requires planning and scheduling, since electrical power is a scarce resource in space. The automation of power system management for future spacecraft has been recognized as an important R&D goal. Several automation technologies have emerged including the use of expert systems for automating human problem solving capabilities such as rule based expert system for fault diagnosis and load scheduling. It is questionable whether current generation expert system technology is applicable for power system management in space. The objective of the ADEPTS (ADvanced Electrical Power management Techniques for Space systems) is to study new techniques for power management automation. These techniques involve integrating current expert system technology with that of parallel and distributed computing, as well as a distributed, object-oriented approach to software design. The focus of the current study is the integration of new procedures for automatically planning and scheduling loads with procedures for performing fault diagnosis and control. The objective is the concurrent execution of both sets of tasks on separate transputer processors, thus adding parallelism to the overall management process

Process: program for research on operator control in an experimental simulated setting

An experimental tool for the investigation of human control behavior of slowly responding dynamic systems is described. Process (Program for Research on Operator Control in an Experimental Simulated Setting) is a simulation of a dynamic water-alcohol distillation system that is especially useful in research on operator training. In particular, Process was developed to conduct research on fault management skill

Multi-Agent Cooperation for Particle Accelerator Control

We present practical investigations in a real industrial controls environment for justifying theoretical DAI (Distributed Artificial Intelligence) results, and we discuss theoretical aspects of practical investigations for accelerator control and operation. A generalized hypothesis is introduced, based on a unified view of control, monitoring, diagnosis, maintenance and repair tasks leading to a general method of cooperation for expert systems by exchanging hypotheses. This has been tested for task and result sharing cooperation scenarios. Generalized hypotheses also allow us to treat the repetitive diagnosis-recovery cycle as task sharing cooperation. Problems with such a loop or even recursive calls between the different agents are discussed

Making intelligent systems team players: Case studies and design issues. Volume 1: Human-computer interaction design

Initial results are reported from a multi-year, interdisciplinary effort to provide guidance and assistance for designers of intelligent systems and their user interfaces. The objective is to achieve more effective human-computer interaction (HCI) for systems with real time fault management capabilities. Intelligent fault management systems within the NASA were evaluated for insight into the design of systems with complex HCI. Preliminary results include: (1) a description of real time fault management in aerospace domains; (2) recommendations and examples for improving intelligent systems design and user interface design; (3) identification of issues requiring further research; and (4) recommendations for a development methodology integrating HCI design into intelligent system design

A hierarchical distributed control model for coordinating intelligent systems

A hierarchical distributed control (HDC) model for coordinating cooperative problem-solving among intelligent systems is described. The model was implemented using SOCIAL, an innovative object-oriented tool for integrating heterogeneous, distributed software systems. SOCIAL embeds applications in 'wrapper' objects called Agents, which supply predefined capabilities for distributed communication, control, data specification, and translation. The HDC model is realized in SOCIAL as a 'Manager'Agent that coordinates interactions among application Agents. The HDC Manager: indexes the capabilities of application Agents; routes request messages to suitable server Agents; and stores results in a commonly accessible 'Bulletin-Board'. This centralized control model is illustrated in a fault diagnosis application for launch operations support of the Space Shuttle fleet at NASA, Kennedy Space Center