Network Rail Maintenance Strategy

Transcription

1 Digital track maintenance case study How to make the move from reactive to proactive approaches to Maintenance & Asset Management - Will data-driven decision making offer you the best opportunity to drive efficiencies? 23-Jan-17 / 1

2 Network Rail Maintenance Strategy Clarify pan-organisational maintenance accountabilities Develop enhanced asset condition monitoring and analytics to predict and prevent asset failure. Apply risk based maintenance regimes. Develop activity-based maintenance business plans Increase maintenance planning capability Agree the trade-off between maintenance access and train services. Create a structured continuous improvement culture /

3 Predict and prevent strategic aim Cost Data FMECA Condition Monitoring Technology Data Analytics Workbank Scheduling Integrated IS Systems Find & Fix Time based intervention Predict & Prevent Condition based intervention Risk based intervention Reliability based intervention Model & Manage System of systems based intervention Resilience based intervention /

4 Predict and prevent organisation Head of Monitoring (Predict & Prevent) Head of Systems Reliability Principal Engineer Fixed Monitoring Principal Engineer Mobile Monitoring Head of Advanced Analytics Business Systems Owner (Maintenance) Team Team Team Team Head of Asset Data Governance Head of Safety Critical Infrastructure Systems /

5 Network Rail key cost items 23-Jan-17 / 5

6 Failure Modes Effects Criticality Analysis (FMECA) Review / update / creation of FMECA is being undertaken o 24% of asset types have FMECA loaded into our fault management system o 59% of key signalling assets have FMECA o 70% exist overall but have been applied inconsistently Provision of FMECA is now mandated as part of Network Rail s Product Acceptance Suppliers have to provide Reliability Centred Maintenance regimes to obtain product acceptance / 6

7 Condition monitoring technology status 23-Jan-17 / 7

8 Train borne progress 23-Jan-17 / 8

9 Impact of improved train borne monitoring In 2015/16 Network Rail had 109 broken rails, a slight increase over the 98 breaks in 2014/15 This represents a reduction of 90% in 15 years against a 50% increase in traffic over the same period /

10 Over 40,000 assets monitored with RCM 35,000 1,000 Power supplies (95%) Rail and equipment room temp 12,000 Points (61%) 18,000 Track circuits (26%) 3,000 Point heating supplies (80%) 1 / 10 0

11 Impact of remote condition monitoring Non-Track Assets (excl Telecoms) - Incidents & DPI Incident Count 45,000 40,000 35,000 30,000 25,000 20,000 41,572 39,265 41,312 42,322 40,374 36,191 35,322 35,101 30,684 30,596 28,758 25,906 24,590 23,613 23,096 21,676 Delay per Incident ,595 20, Incident count (Service Affecting Failures) DPI 15, ,000 5, /

12 Taking P&P forward in Network Rail ISO is being utilised to drive a standardised approach Strategy and benefit models are aligned to the framework outlined in ISO State Detection enables business benefit Higher Level Analytics offers incremental benefit opportunities at affordable cost Presentation Title: View > Header & Footer / 12

13 Availability & Granularity of Data Network Rail data is short of the quality and granularity that we will need to fully deploy Predict & Prevent maintenance Use & Location Industry Bus. Category Installation Plant / Unit Section / System Data held to support renewals decisions Equipment Subdivision Equipment Unit Sub Unit Maintainable Item Part Data held in Ellipse to support maint. decisions - needs improving New data required - several million data points Data needs - taxonomy ( source ISO ) / 13

14 Use of Advanced analytics proof of concept Fault diagnosis for points and prediction of time to failure of track circuits Expert systems for Points monitoring Bayesian Reasoning for track circuits All solutions must consider the human factors Pilot planned for mid 2017 Collect fault-free and extreme faulty data Identify areas of interest using relevant analysis techniques and use them to create rules Apply those rules to other waveforms and match similar features using the trained fault data as a basis for scoring 40% IET Conference Birmingham 7th May 2015 / 14

15 Decision support capability at end of 2018 DATA CAPTURE DATA BUILD DATA COLLATION DECISION SUPPORT & REPORTING TOOLS DECISION MAKING Measurement Trains Remote condition monitoring Maintenance Teams using ORBIS Apps ACTRAFF GPR CARRS TICLED Ellipse AINDMT TRS RDMS SSADS DCS Trust CDMS JBA OPTRAM 22 systems FMS RDMS FME Intelligent LADS Infrastructure Weather integrated Trust ARMS OPAS 15 more Footfall CRT NHD Sustainability currently PPS 2018 RST Current Sources Release 1.3 GEOGIS/ INM Station view planned by end Curve Wear Tie bar Primavera SMISCV ALCRM WCCTV Release 1.4 Release 2.0 ASSET DATA STORE ADS ADS OPERATIONAL DATA STORE STAGING AREA ANALYTICS AD-HOC REPORTING & DASHBOARD TRACK & S&C SIGNALLING E&P OPS PROPERTY LEVEL XINGS Performance Capability Risk Safety Predict OPTIMISED WHOLE LIFE COST PERFORMANCE TRADE OFFS EFFICIENT MAINTENANCE ENHANCED RISK BASED MAINTENANCE REGIMES OPTIMISED RENEWALS SELECTION OF ASSET TYPE Reliable efficient maintenance Optimised interventions Improved work planning /

16 Conceptual Target Architecture DATA CAPTURE AND FAILURE MODES DATA CAPTURE As existing plus: Service train measurement data Weather SCADA Rolling stock data Digital railway FAILURE MODES Fault records FMECA Mean time to failure INPUTS DATA ANALYSIS CROSS DISCIPLINE DECISION SUPPORT TOOLS Real Time Analytics Failure detection ANALYSIS Predictive Modelling Platform ADS Intelligence DECISION SUPPORT & REPORTING TOOLS INTELLIGENCE Integration Correlation Reporting MODELLING P&P Asset Health Criticality ALERTS Prognostics Advisory Gen. /

17 Target strategic outcome Optimised whole life cost point of renewal High Total Lifecycle Cost Annual Average Cost Low Minimum Risk Cost Totex Cost High Frequency of Intervention Low /

18 In summary Single guiding mind is key Fitment of additional assets needs to be carefully considered Data sets must be consolidated and linked Data collection processing doesn t have to be expensive. Reaching world class predictive, risk and condition based maintenance strategies can save: 25-35% reduction in total expenditure 35-45% reduction in down time following failure / 18