RailSense. Improving the Reliability, Availability, Maintainability and Safety of rail systems using complex monitoring and model-based data analysis

Transcription

1 RailSense Improving the Reliability, Availability, Maintainability and Safety of rail systems using complex monitoring and model-based data analysis Dr. E.J. de Geus DySI Ontwikkel B.V., the Netherlands & Ir. P. Boom Lloyd s Register Rail Europe B.V., the Netherlands peter.boom@lrrail.com

2 1500V 500V 220/380V (lokaal net) 220/380V (treinstelnet) 110V 24V microfoon WIFI GPRS microfoon WIFI RailSense 1. Introduction 2. RailSense: general approach 3. Specific application 4. Conclusions mbvk1 ABv6 ABv3/4 mbvk2 CC diagnose VIRM Trainbox Meetcomputer (DYSI/Lloyd s) Meetcomputer (DYSI/Lloyd s) Smart connection: Ethernet + WIFI + connectivity naar wal

3 Introduction LRR (Lloyd s Register Rail) Rail asset intelligence (fleet and infrastructure); monitoring, performance and on-board IT (a.o.) DySI (Dynamic Systems Intelligence) Complex, multi-variate data analysis and empirical modelling (a.o.) Joint project combining railway expertise of LRR with innovative data analysis and computer modelling tools of DySI

4 Introduction LRR (Lloyd s Register Rail) Expert infrastructure and rolling stock knowledge Expertise with rail asset protection and monitoring systems Gotcha: track-based rolling stock monitoring T 2 IMS: on-board Train Track Interface Monitoring System DySI (Dynamic Systems Intelligence) Experience with complex monitoring & model-based analysis High-performance software: enabling software to run in real-time LOFAR (world s largest radio telescope, sensors, 26 Tb/s, 100 Tflops)

5 RailSense General Approach sensors analysis monitoring Data engineering object Business rules Model-development actuator Information/ knowledge feed-back/ feed-forward

6 RailSense General Approach Objective: improve Reliability, Availability, Maintainability and Safety (RAMS) of rail system and its components in an innovative analytical way. RailSense Extract useful information from complex data of large numbers of sensors through: Real-time data analysis Model-based analysis Multi-variate analysis (time-shifted) correlations Build standard models of sub-systems (traction, engine, etc) Facilitates finding of deviations, improved (anticipatory) maintenance planning, automatic feedback into system

7 1500V 500V 220/380V (lokaal net) 220/380V (treinstelnet) 110V 24V microfoon WIFI GPRS microfoon WIFI RailSense General Approach Electrical system Pantograph/hi-voltage system interface Transformers and switch boxes Electromotor - drive-train system Mechanical system train train, train infra & infra train on-board platforms & Gotcha Adding sensors (sound, vibrations, temp, humidity, CO 2, GPS, etc) Data fusion, correlations, statistics, complex cause-effect chains Involve existing databases (R5, Gotcha, ) Exploratory, multi-variate data analysis Serendipity! mbvk1 ABv6 ABv3/4 mbvk2 CC diagnose VIRM Trainbox Meetcomputer (DYSI/Lloyd s) Meetcomputer (DYSI/Lloyd s) Smart connection: Ethernet + WIFI + connectivity naar wal

8 Specific Application Measuring behaviour electromotor of one specific train type Goals: increase reliability and availability Monitoring 29 analog signals, 32 digital signals, 6 counters, 4 audio signals (analog) Sampling between 1 and 30 khz Approx. 400 GByte of data per week Automated data analysis Correlations, thresholds, fault-finding, etc Complex cause-effect chains Models being constructed of 4 different subsystems

9 Specific Application Data Current Currents 1 & 3 Voltage UV (sparks)

10 Specific Application Model-based Analysis Monitoring data analysis BlackBoard Statistical Analyses Gear settings Motor I,V Environment Vibrations Control systems tresholds distributions, deviations trending normalisation models Traction model Gear Model Safety settings Model Multi Variate Analyses correlation PCA, factor analysis Mechanical model PLS & classification Motor Model cluster interpretation & reporting

11 Specific Application Conclusions Information content very large Sufficient for model construction Sufficient for searching complex cause-effect chains Measurements have already surprised experts Therefore: analysis should be Multivariate Exploratory Model-based in order not to be trapped by pre-conceptions

12 RailSense Conclusions Generic approach successfully introduced in rail environment Broad monitoring essential to find complex cause-effect chains Models important for optimal dynamic maintenance scheduling & analysis of norm-deviations Can be implemented by any rail operating company, rail infrastructure manager or rolling stock maintenance company. Combination of specialist data and modeling capabilities of DySI with extensive rail expertise of LRR can deliver substantial value to business performances. Results of the RailSense project show promise to contribute to a step change in the improved performance of rail assets.

13 DySI and Lloyd s: partners that make RailSense