Insulation Resistance Example Sudden jump in voltage SCM 902 61
Insulation Resistance Example SCM 902 Sudden increase in interference 62
Insulation Resistance Example SCM 902 effected more than SCM 901 SCM 902 SCM 901 63
Insulation Resistance Example • Insulation resistance • Communication failure rate • SCM house keeping • All implicate SCM 902 more than SCM 901 • Due to inductive couplers at the SCMMB the SCM can be ruled out • This identified the SDU or TSCJ for SCM 902 as the most probable source of the fault 64
Insulation Resistance Example Probable location of fault 1. Umbilical 2. UTA to SDU jumper 3. SDU 4. TSCJ to 901 5. TSCJ to 902 65
Insulation Resistance Example Probable location of fault 1. Umbilical 2. UTA to SDU jumper 3. SDU 4. TSCJ to 901 5. TSCJ to 902 66
Insulation Resistance Example Location of the fault TSCJ connector to SDU 67
Insulation Resistance Example 68
Insulation Resistance Example • No condition monitoring: • Long lead items 16 week delivery • Manufacturing 2 to 4 weeks • Lost production 10,250 bpd • Lost production £59,340,000 Note: • Calculation based on 20 weeks lost production, Brent Crude oil price (19/04/2017) of $53.01 per barrel and an exchange rate of $1 = £0.78 the lost production would have been £59,334,093. • Based on the Brent Crude oil price at the time of the event, 29/01/2013 of $115.22 per barrel and an exchange rate of $1 = £0.6351 the lost production would have been £105,007,878. 69
Insulation Resistance Example • With condition monitoring: • Condition monitoring gave 17 months notice of a failure • Spares were purchased and dive procedures written; • The most probable location of the failure was identified prior to the DSV sailing 70
Insulation Resistance Example • With condition monitoring: • Production was restored within 8 days of the failure • Lost production £3,390,000 • Saving £55,950,000 Note: • Based on the Brent Crude oil price on 29/01/2013 of $115.22 per barrel and an exchange rate of $1 = £0.6351 the savings were £99,007,427. 71
CASE STUDY # 2 72
Data analyst SME Joint Cognitive Post-doc data scientists working with Wood Group System Approach dedicated to predictive maintenance development (Human + Ai) 73
Asset map view dashboard reading latest CBM data / reporting Degrading, failed or other watch list equipment flagged with clickable links to key sensor data Summary data for all map components available on hover 74 Time Series Data Analysis, Diagnostics and Prediction
CASE STUDY # 3 75
ASD pattern detection signal parameter tuning GUI Automated alarm signal extraction Automated detection and and classification visualization of concurrent events Choke valve movement + ASD event 76
Driving disruptive change in inspection and - Subsea infrastructure monitoring - AUV Pipeline inspections - Infield ROV for normal infield inspection - Less focused - Value-driven, inspection and monitoring Using machine vision to automatically detect anomalies - Drone inspection of onshore - Why to inspect and what for? pipelines/infrastructure - Focus on leading / lagging indicators of failure - Safety-driven inspection innovation
Data is only as good as the sensors producing it and their reliability - Communications - Discrete sensing - Distributed sensing Evolution from all data flowing back to value - Relatively low cost and reliable data flows enabled by analytics in the field - Potential for all-fibre-optic monitoring (requiring less bandwidth) systems? (could this be a realistic offering?)
Summary • Industry and economic forces driving disruptive change • Search for efficient, lower cost, risk-based, innovative and reliable solutions • Key developments – value-driven (smart) inspection and monitoring – autonomous and robotic vehicles and inspection systems – data analytics and edge analytics – Fibre-optics monitoring/sensing and comms. – Innovative technologies that deliver value • Need to adapt to survive…. 79
Subsea Condition Monitoring and Predictive analysis Subsea UK, NSRI, The DataLab and CENSIS workshop 25 April 2017
The Innovation Centres Introduction & what we do Case studies Trends in SIS Subsea, trends and sensing
The Innovation Centres
Innovation ecosystem - Scotland Scottish European UK Scottish Scotland Government Enterprise Europa Union Government West of North of Scotland Scotland KTP SMAS Centres Highlands & Islands East of Scotland Enterprise SDI Innovate Scottish UK Businesses Scottish Scottish Scottish Energy Renewables Association Funding Council Industry Interface SHIL Technology Facilitator Trade Fraunhofer Scotland Applied Scottish Associations Food & Photonics Engineering Drink James TUV Hutton NEL Fisheries Intermediaries Institute Technology Innovation Scotland Scotland LINC ScotlandIS RTOs Scotland Scottish Moredun Association Research Marine Targeting CeeD Institute Science Innovation DHI Cell & CSIC Scotch AFRC OGIC Roslin Whisky Gene Research Institute Therapy CSA Institute ORE SULSA SINAPSE SFC SAIC Innovation SIRE MASTS CENSIS Digital HVM Centres Catapult Scottish Centres Energy The SAGES ScotCHEM Universities Systems IBioIC Data SoXSA Lab SMS-IC Future SICSA SRPE Transport Cities Systems Medicines Precision ETP SUPA Discovery Medicine Scottish Innovation Ecosystem
Direct Support: Glasgow and Aberdeen Space, Skills and Stakeholders Supply Chain & Software and Support Research Staff • IoT Centre • Integration - SE/HIE/SDI and • Full Scottish supply • Industry-experienced Govt packages chain engineers • Engagement & referral- inc • Researchers across all • Hardware, software and Catapults, other ICs and HEIs tools industry bodies • Hot desk space
Progressing new products and markets
IoT Centre An Identified need for end to end mentoring Enabled delivery of IoT Boost SME challenges fast tracked into IoT products and services • Demo space, drop-in centre, seminars, mentoring • Engineering support Developed with support that includes: Av. 1 per month since launch mid 2015
Using best in class /disruptive IoT device enablement tools and kits R Multi protocol A platform 6lowpan D I Wifi, BLE, Wifi Wifi Xbee Zigbee BLE 6lowpan,Zigbee O M RTK TI CC2650 TI CC3200 I Red Pitaya C Arduino Beaglebone R O Raspberry Pi Libellium Waspmote Intel Galileo Freescale Freedom S E N Barometric Pressure IR camera 9 DOF IMU S GPS O Gas R PIR (motion) Temperature Humidity Ultrasonic range finder Camera
Engagement models Vision Feasibility Review & Positioning Supporting Planning & Scoping projects IoT Centre Demonstrators and test beds Funding and Partnering
Case studies
Challenge Project Impact • Low-pixel • • UWS expertise in Auto detect people automatic target image processing and vehicles and recognition and object detection algorithms • Identify vehicle • Recognition of type objects based on • Develop algorithms data from sensors using low-resolution • Operate in real thermal image data time with limited processing hardware Partners: Thales UK Glasgow, CENSIS and the University of the West of Scotland
Challenge Project Impact • Environmental sensing • Sensing Environmental Risk • Increase confidence in data. measures CO2, wind speed, • Statistical models from St Andrews Uni • Better understanding of precipitation, humidity, sulphates, particulates etc. incorporated to Topolytics SW environmental impact platform for real-time, accurate gas • ‘Data noise’ creates • More effectively monitor waste emissions challenges: how do we filter • In collaboration with: • Potential new commercial innocuous readings from those requiring action? offerings in a market valued at $20Bn by 2020 • Sensors age and degrade over time: how do we ensure data A ' N E R A F O reflects conditions on the ground? Partners: Topolytics, University of St Andrews and CENSIS
Challenge Project Impact • Data gathered by Scottish Water in • Decision support for management of • Transforming asset management operational environments is not being water and wastewater assets used to extract maximum value • Timely and planned intervention for • The establishment of a high-quality repairs and maintenance • SW assets often situated in remote/ decision support environment to demanding environments optimise equipment in the field • Significant cost savings • Signal processing advances can bring • Potential worldwide interest new value to existing data • New techniques will identify key signatures in data Partners: Scottish Water, CENSIS and University of Strathclyde
Trends in SIS
Trends and drivers Drivers Challenges Trends Battery life, deep sleep, IoT, wireless Power efficiency energy harvesting sensing Fusion & Packaging, accuracy and Smart devices, Miniaturisation sensitivity wearables Local processing, Intelligent LPWAN, distributed bandwidth considerations edge devices networks Ubiquity, ’lick and stick’ Cost reduction Sense everything Remote Spectrum space, bandwith, Diverse monitoring range, quality of signal, real communications time & more
Servitisation Source: PWC Industry 4.0 Building The Digital Enterprise
Mass Production $
Product with Parts $ $ $
Product with SLA - Servitisation $ $
Connectivity and Service Optimisation $ $
Data Monetisation $ $ $
Information Ecosystem $ $ $ $
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