Fault Diagnosis System for Wireless Sensor Networks Praharshana Perera Supervisors: Luciana Moreira Sá de Souza Christian Decker.

Fault Diagnosis System for Wireless Sensor Networks Praharshana Perera Supervisors: Luciana Moreira Sá de Souza Christian Decker

of 23 Outline Introduction Sensor Data Analysis Data Correlation Time Dependant Sensor Data Analysis Approaches Neural Network based Fault Detector Rule Based fault Detector Evaluation Evaluation Neural Fault Detector Evaluation Rule based Fault Detector Conclusions and Future Work

of 23 Introduction Wireless Sensor Networks have the potential to be used in the near future in industrial applications: Inventory Management Items Tracking Environment Health and Safety Monitor Storage Regulations Monitor Patient Conditions Track Personnel (Workers in Hazardous Areas)

of 23 WSN Failure in a Business Process WSN Effects of failures in a Business Process: Economic losses Contamination of the environment Human life risk Quality reduction Maintenance costs

of 23 Our Goal Automatic identification of incorrect sensor readings Called value failures Provide a higher maintainability to the business process by Diagnosing failures before they propagate further to the rest of the system

of 23 State of the Art Value Fault Detection for WSNs Depend heavily on model assumptions and expert knowledge Lack prior data analysis Perform fault detection in nodes itself Hierarchical detection does not provide value failure detection but shift the task of fault detection to a more powerful device (sink) WSN

of 23 Neural and Fuzzy Models in Sensor Fault Detection Advantages Ability to learn any complex system model No assumptions on mathematical/statistical models Less expert knowledge Disadvantages Require training time Scalability for WSNs

of 23 Analysis - Sensor Data

of 23 Analysis - Incorrect Sensor Readings 4 abnormal peaks of temperature sensor data Light sensor stuck in one value

of 23 Sensor Data Correlation Metrics Correlation coefficient Multiple correlation coefficient Gathered Data Temperature, Light, and Movement data of 3 neighboring nodes 3 days To reduce noise (especially movement and light) Interpolation Moving Average Results SensorMultiple correlation coefficient Temperature0.91 Light0.93 Sensor Correlation coefficient TemperatureLight0.73 TemperatureMovement0.69 LightMovement0.69 xx yy HighLow

of 23 Time Dependant Sensor Data Analysis

of 23 Neural Network based Fault Detector A neural network has the capability of learning these patterns Requires training data A neural network is trained to identify Too high (incorrect) Too low (incorrect) Normal (correct) Temperature Sensor readings

of 23 Rule based Fault Detector Rule based fault detection algorithm Rules search phase Online fault detection phase Rules are discovered automatically eliminating the need of an expert Sensor Data Statistics σ μ R r Input Rule Base Threshold rulesFuzzy rules Fault Detection Output Valid/Invalid

of 23 Rules Search Phase Threshold Rules Expected values for a node for the time period T Mean μ Standard deviation σ Multiple correlation coefficient R Correlation coefficient r Threshold Rules Search Fuzzy Rules Search Input (Statistics for Time period T) σ μ R r (Rules for Time period T) Output If T then μ ≈ X If T and σ = low Then R = high Fuzzy Rules Relationships between statistics for a node for the time period T μ different sensors σ and R same sensor r different sensors

of 23 Fault Detection Phase Time Period T Sensor Measurements μ σ R r Sensor data Preprocess Rule Base Threshold rules Fuzzy rules Threshold Rules If no rule is rejected If majority of the rules is rejected Else correct incorrect Fuzzy Rules Validate corresponding fuzzy rules If rejectedincorrect

of 23 Evaluation Experiment setup 32 nodes (uParts) deployed on the ground floor Data collected for a time period of 23 days (3 for training) Evaluation Metrics False positive effectiveness (FPE) = actual unreliable / identified unreliable Fault detection effectiveness (FDE) = identified unreliable / unreliable

of 23 Evaluation – Neural Fault Detector Experiment results Fault detection effectiveness (FDE)False positive effectiveness (FPE) 0.750.80

of 23 Evaluation – Rule based Fault Detector Identified Rules Temperature Light

of 23 Evaluation – Threshold Rules

of 23 Evaluation - Number of Rejected Threshold rules

of 23 Evaluation – Rule based Fault Detector Example

of 23 Conclusions and Future Work Conclusions Proved to be efficient on identification of failures A new strategy to evaluate sensor readings in WSNs Require less expert knowledge of the system Ability to learn environment and system dynamics Fault detection performed in back-end Without putting burden on the nodes Independent of any hardware platform :- Ideal for enterprise scenarios Neural fault detector :- potential to be used in specialized scenarios Rule based fault detector :- Any WSN scenario supporting the users (operators)

of 23 Conclusions and Future Work Future Work Evaluating the approaches within a second application trial Long period of time Introducing errors Neural network to detect failures in light and movement sensors Enhancements in the decision scheme in rule based detector Voting or weighting mechanisms

of 23 Faults in real WSNs application deployments Failures Water enters the enclosure Battery depletion Software bugs Unreliable links Circular routings Intermittent connection of the sink with the back-end Sink failures

of 23 Fault Detection in Industrial Applications Major research topic in the past few decades Focus on learning the system model System models can be Analytical Neural Fuzzy Examples: Validate sensor readings in one of the NASA space shuttle engine Fuzzy fault detection systems based on Wang and Mendels model Unfortunately not used in fault detection for WSNs

of 23 Sensor Data Correlation There exists a potential for sensor data correlation Can a fault detection algorithm solely based on correlation?

of 23 Effect of Moving Average (Movement Data)

of 23 Time Dependant Sensor Data Analysis

of 23 Conclusions of the Analysis Models derived solely based on overall sensor data loose meaningful information and characteristics There exists a potential for data correlation between sensor readings The time dependant analysis of sensor data offers the possibility of deriving meaningful models Possible fault detection approaches Neural Network approach Rule based approach

of 23 Neural Network Architecture Characteristics Three layer feed forward neural network Each node has a bipolar sigmoid activation function Inputs Normalized temperature sensor readings Outputs Normal = 0 Too high = 1 Too low = -1 Bipolar sigmoid activation function

of 23 Implementation - Part of the FTCoWiseNets Framework

of 23 Implementation Data Base MySQL Fault Detection Application Java – Scilab API Scilab - Environment NeuralRule based

of 23 Evaluation – Neural Fault Detector Training size Error Time Experiment results

of 23 Evaluation – Threshold rules + Fuzzy Rules

Fault Diagnosis System for Wireless Sensor Networks Praharshana Perera Supervisors: Luciana Moreira Sá de Souza Christian Decker.

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