Fault Diagnosis System for Wireless Sensor Networks

Praharshana Perera

Supervisors: Luciana Moreira Sá de Souza Christian Decker

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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

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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)

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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

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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

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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

WSN

WSN

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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

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Analysis - Sensor Data

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Analysis - Incorrect Sensor Readings

4 abnormal peaks of temperature sensor data

Light sensor stuck in one value

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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

Sensor Multiple correlation coefficient

Temperature 0.91

Light 0.93

Sensor Sensor Correlation coefficient

Temperature Light 0.73

Temperature Movement 0.69

Light Movement 0.69

x x

y yHigh Low

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Time Dependant Sensor Data Analysis

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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

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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

SensorData Statistics

σ μ R r

Input

Rule Base

Threshold rules Fuzzy rules

Fault Detection

Output

Valid/Invalid

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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 RulesSearch

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 = lowThen 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

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Fault Detection Phase

Time Period T

Sensor Measurementsμ 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 rejected incorrect

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EvaluationExperiment 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

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Evaluation – Neural Fault Detector

Experiment results

Fault detection effectiveness (FDE) False positive effectiveness (FPE)

0.75 0.80

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Evaluation – Rule based Fault Detector

Identified Rules

Temperature

Light

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Evaluation – Threshold Rules

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Evaluation - Number of Rejected Threshold rules

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Evaluation – Rule based Fault Detector

Example

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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)

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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