Mission Readiness & Cost Management using edna. The DEI GROUP, Inc

Transcription

1 Mission Readiness & Cost Management using edna The DEI GROUP, Inc

2 Who is The DEI Group? The DEI Group is an asset management technology company, and a long time InStep Software Partner, providing machinery information systems based decision support solutions We understand Asset Management leading business practices and Associated Systems: The DEI Group has the breadth and depth of asset management related processes and information systems expertise gained over 33 years of working with US Navy, maritime, energy and other asset intensive industries Our Methodology and Technology Enable & Ensure Maintenance Excellence : RCM/FMEA, Predictive Risk-based analysis, and machinery condition assessment technologies integrate all aspects of asset management objectives definition, process & systems design with plant systems Delivering successful Asset Management solutions on time and budget: We have deployed small, highly qualified teams that provided major benefits through leading practices solutions to a range of industrial and military operations such as US Navy, GE Energy Systems, Exelon Generation, Mittal Steel, Chevron

3 Industry Critical Need: Implementing an Integrated Predictive Asset Management Program 1. Redesigning the Equipment Maintenance Strategies Maintenance strategies, standard job specifications, and maintenance processes based on reliability best practices using RCM/FEMA approach: Elimination of Calendar Based Maintenance & Inspections not supported by reliability analysis 2. Preventing Failure During Production Periods Continuous equipment performance visibility Early detection of machinery condition change Current equipment condition estimation Failure Risk prediction accounting for planned operating tempo 3. Implementing Automated Advance Planning & Scheduling of Work to be Performed Define what needs to be done with at least an 80% confidence factor Define when the work needs to be done Define why the scope needs to be done

4 Predictive Asset Management: Combining historical with the real time data to better predict the future PREDICTIVE ASSET VALUE MANAGEMENT What is going to happen? What is happening? What happened? Operational impact Logistics requirements Residual life estimation Prognostics KPI s (leading) Performance monitoring Diagnostics Condition monitoring Configuration & event management RAM monitoring Benchmarking (KPIs) Maintenance Data

5 DEI Group Core Offering: Reliability Engineering Derived CBM Implementation of Condition Based Maintenance capability that integrates equipment/system predictive failure risk assessment, maintenance practices and maintenance processes in a close loop system to enable: Equipment/system failure risk assessment (which equipment failure mode has a future risk of occurrence?) What failure and how long until failure Maintenance advance planning and scheduling (when should the work be done) What needs to be done and when is the best time for the work to be done Maintenance best practices (preplanned and positioned parts, tools, tech data, processes needed to execute work) How effectively can the work be done Cumulative Probability Time to failure Gives time to Make decision on Planning and Scheduling Process

6 What is Predictive Machinery Analyzer (PreMA)? COTS software for on-line automated machinery condition monitoring, diagnostic and prognostic enabling Condition Based Maintenance (CBM), Remote Monitoring, & Remote Technical Assistance ELEMENTS OF PreMA SOFTWARE Operating System PreMA software (edna, Diagnostics and Prognostics) Over 30 Industry Standard interfaces Modbus, OPC, MIMOSA, etc Hardware CPU: 2 CPU, Intel, Dual/Quad core >2.5 GHz RAM: At least 24 GB HDD: At least 500 GB Media: DVD-RW NIC: Gigabit Ethernet I/O: Mouse, keyboard, monitor OS: MS Windows 8 Pro, MS Windows Server 2008 R2 or 2012 SW: MS Office Pro (2007/2010) UPS: 15-minute life with shutdown software link edna Smart Sensor integration Motor Condition Monitor (MCM) Vibration Lube Oil Diesel Doctor 6

7 PreMA Capabilities CBM implementation tool Machinery Data Trending Rules based expert system Predictive Analytics Vibration Analysis Fluid Quality Monitoring Troubleshooting Aid Rules based expert system Event capture Operational Assessment Material Assessment (Current Risk) Plant Situational Awareness Visual Assessment Support PreMA Operational/ Maintenance Awareness : What's the failure risk in 30 days for now? performs a continuous analysis of machinery data Continuous Analysis automatically generates maintenance advisory 7

8 Available Sensors Fuel Oil Pressure Sensors EGT Imbalance Crank Case Pressure CYLGrp 1-2 Low edna BBN Engine Failure Modes Current Health Status SENSORS SMART SENSORS Bad Combustion Injector Wear Steady State Ops Performance Analysis BBN Diagnostics Fuel Pump Cylinder Rings Hours &Transient Ops Operating hours & Stress Inducing Events Current Reliability/ Cumulative Damage Reliability Model Engine Working Age Predicted Reliability Reliability Model Reliability Prediction

9 Project Summary: Navy LCS Class Designing and implementing Commercial Best Practices for the maintenance management process Performing Reliability Engineering functions to the US Navy since 2008: Developed Maintenance Strategies (RCM /FMEA/ Job plans) for 23 systems for 2 ship classes (approximately 11,000 failure modes) based on Metrics and Risk to Mission Readiness Implemented the Navy next generation condition monitoring and prognostics (PreMA) that includes mobile device for portably collected data Integrated the CBM system, mobile, and logistics modeling with commercial EAM system for advance planning based on equipment failure risks Implemented and evaluated Smart CBM sensor technologies including on-line lube oil monitoring, vibration monitoring and motor condition monitoring (MCM)

10 Tremendous Benefits Medium Pressure Air Compressor Parts, Labor, Availability Comparisons

11 Navy SSDG Governor failure: Use of a Data Historian for Forensic Analysis and Training a Diagnostic Module Observation SSDG 3 suffered a governor failure on March 23, 2013 Due to insufficient history, a governor failure model was not deployed within PreMA and it was not part of the initial set of CBM monitored failure modes Action Taken After the SSDG 3 failure a forensic analysis was executed to identify ability for symptom recognition, and to develop a failure model to be deployed in PreMA Findings The baseline (good performance data) and degraded data (leading to failure) collected was clean and sufficient to allow analysis and development/testing of a new governor predictive failure model SSDG Governor Performance Model, based on a performance index principle, was successfully built and implemented at a later time Issue Prevented: The model was tested against the baseline and degraded SSDG data sets, and it correctly provided an advance indication of pending failure on a second ship that the model was implemented Lessons Learned: The high fidelity Data Historian component of PreMA provides an excellent basis for continuous validation and expansion of diagnostic and prognostic capability, when sufficient design or operational information is not available during initial system implementation

12 SSDG Governor Use of a Data Historian for Forensic Analysis and Training a Diagnostic Module Good data Baseline Warning Degrading Governor Performance Index Fast developing failure, under 8 hours from inception to failure: PreMA was able to trigger alarm for operator intervention Giving ship s crew enough time to take relevant action and prevent a catastrophically failure Note: Zero or Near-Zero values indicate a stopped engine

13 SDG 4 Re-Build Avoiding a costly un-needed Preventive Maintenance CBM Recommendation Based on evaluation of Operational Test, no major maintenance is recommended on SDG #4 Action Taken by the Navy OEM recommended, and the Navy agreed to allow OEM to perform rebuild of SDG #4 Top End during scheduled availability, as per their 8000 hr PM task in tech manual to be used as CBM Reliability Model validation and verification Findings During disassembly and inspection, it was found that the 8000 hr re-build was not needed, and analysis based conditions were verified and validated Lessons Learned: CBM is a valid strategy that allows performing maintenance only upon evidence of need without incurring reliability risks

14 SSDG4 EGT vs. kw : edna data analysis Cylinders EGT baseline curve SSDG4 EGT vs. kw (7/20-7/21) EGT Out baseline curve DGEGTManA1_2LTI DGEGTManA3_4LTI EGT (deg F) the data cluster is within acceptable range to baseline DGEGTManB1_3RTI DGEGTManB2_4RTI DGEGTOut Poly. (EGT Exp) kw

15 Project : Shipping industry client operates 28 tankers 1. Develop and implement standard job plan for PM tasks across tanker fleet Perform FMECA Engineering Analysis and develop maintenance strategy documents for identified assets for 22 ships Identify failure modes, causes, symptoms and sensor requirements to monitor system performance Recommend Job trigger and intervals (calendar/ Run Hours/ Condition Based) To date over 4000 jobs plans and strategies were developed and successfully implemented Ships implementation of PreMA for Blackout reduction Provide visibility into shipboard power systems with PreMA to enable early detection of system anomalies to quantify system health Forecast remaining service life of electrical system components and prioritize the importance of needed repairs Improve operational processes with enhanced Operating Procedures, SOP s, Check Lists, etc Reduce and eventually eliminate loss of power events or Blackouts Example: Defer hours Engine Overhaul based on PreMA analysis Report Summary: Following components are under the PreMA sensor based performance monitoring and reliability models were developed to assess the reliability and predicted remaining life at the point of the planned Engine Overhaul maintenance activity: Fuel Injectors Piston Rings Cylinder Liners Cylinder Intake/Exhaust Valves Connecting Rod Bearings & Main Bearings All above mentioned components had current reliability greater than 70%. From current health perspective, none of the failure modes were active to a level requiring repair Recommendation: Maintenance could be deferred 6000 hours within acceptable risk

16 Reliability numbers, what do they mean? One goal of PreMA prognostics is to predict when a component can no longer fulfill a desired functional requirement This time point is defined as the end of life (EOL), and the amount of time until that point is reached is defined as the component s residual life estimate (RLE). Prognostics is based on analysis of component failure modes and detection of early signs of wear, aging, and fault conditions, which are then incorporated in damage propagation models to predict future damage. Our prognostic models are based on probability density functions and the total area under their graphical depictions represent the complete life of components (which by definition equals to 100%). Reliability is therefore a number between 100% and 0% that naturally accounts for the effects of aging when the component operates under a variety of conditions. If the reliability is, for example, 95% it always means that the component has a relative long expected life ("healthy") in contrast 5%, it always mean that it has a relative short expected life ("deteriorated").

17 ADE No.3 Components Current Reliability as of September 10, 2015 Current Reliability ( as of Sept 10, 2015, before the the maintenance Overhaul ) (Threshold for Suspension = 40%; 100% represents good as last overhaul) Fuel Injectors Piston Rings Cylinder Liner Exhaust Valves Intake Valves Conn. Rod Bearings Main Bearings Cylinder No.1 Cylinder No.2 Cylinder No.3 Cylinder No.4 Cylinder No.5 Cylinder No.6 Cylinder No.7 83% 69% 74% 65% 78% 83 +/- 2.5 % 83 +/- 2.5 % 85% 69% 73% 69% 81% 83 +/- 2.5 % 83 +/- 2.5 % 86% 69% 74% 70% 82% 83 +/- 2.5 % 83 +/- 2.5 % 86% 69% 74% 70% 82% 83 +/- 2.5 % 83 +/- 2.5 % 85% 68% 73% 70% 82% 83 +/- 2.5 % 83 +/- 2.5 % 84% 69% 74% 68% 80% 83 +/- 2.5 % 83 +/- 2.5 % 86% 70% 75% 71% 83% 83 +/- 2.5 % 83 +/- 2.5 % Summary before the Maintenance Overhaul All components have reliability between 65% and 83%. Current health: none of the failure modes are active (no sufficient deviation from expected design curve is observed) and are all within normal range 17

18 Operating Load Profile Operating load profile of ADE #3 since 02/05/2015 The maximum KW rating for these engines is 910 KW This engine was operated under 38% Load (350 KW) for 48 % of the total operating time during the analysis period Note: Lower load is stress inducing factor due to incomplete combustion, which causes accelerated aging and increasing wear rate on engine components like cylinder head valves, turbocharger, fuel injectors, piston rings & liners. 18

19 Fuel Injectors ( before the Maintenance Overhaul ) % Current Reliability (Threshold for Suspension = 40%) Residual Life Estimation (RLE) (Assuming suspension at 40% Reliability) % Current Health (Normal: 100% to 75%; Warning:75% to 60%; Alarm:<60%) Conclusion report 1. Fuel Injectors have an estimated MTBF of 10,000 Hours 2. RLE: average of 6004 hours 3. Current reliability is estimated to be between 83% and 86% 4. None of the fuel injectors failure modes are active, all within normal range. All of the Injector Health calculations are within acceptable limits. CYL 1 83% % CYL 2 85% % CYL 3 86% % CYL 4 86% % CYL 5 85% % CYL 6 84% % CYL 7 86% % Conditions Found During the Maintenance Overhaul Inspection All injectors were pressure tested before start of the overhaul and their operation was confirmed to be satisfactory. Conditions correlate well with the PreMA analytics, with no adjustments required for the models. 19

20 Piston Rings before the Maintenance Overhaul Conclusion Report 1. Piston Rings have an estimated MTBF of 20,000 Hours 2. RLE: average 7730 hours 3. Current reliability is estimated to be between 68% and 70%. 4. Current health, none of the piston failure modes are active % Current Reliability (Threshold for Suspension = 40%) Estimated Remaining Useful Life (RLE) (Assuming suspension at 40% Reliability) CYL 1 69% % CYL 2 69% % CYL 3 69% % CYL 4 69% % CYL 5 68% % CYL 6 69% % CYL 7 70% % % Current Health (Normal: 100% to 75%; Warning:75% to 60%; Alarm:<60%) Conditions Found During Inspection All piston rings were found in tact and moving freely in the grooves. All piston ring groove clearance measurements were found to be satisfactory. Conditions correlate well with the PreMA analytics, 20 with no adjustments required for the models

21 Exhaust Valves ( before the Maintenance Overhaul ) % Current Reliability Estimated Remaining Useful Life (RLE) % Current Health (Threshold for Suspension = 40%) (Assuming suspension at 40% Reliability) (Normal: 100% to 75%; Warning:75% to 60%; Alarm:<60%) CYL 1 65% % CYL 2 69% % CYL 3 70% % CYL 4 70% % CYL 5 70% % CYL 6 68% % CYL 7 71% % Conclusion Report 1. Exhaust valves have an estimated MTBF of 30,000 Hours 2. RLE: average of 11,639 Hours 3. Current reliability is estimated to be between 65% and 71% 4. Current health, none of the exhaust valve failure modes are active and are within normal range Conditions Found During Inspection There was no indication of any cylinder valve and valve seat erosion, burning, or heat damage. Valves were cleaned and re-used. Conditions correlate well with the PreMA analytics, with no adjustments required for the models. 21

22 Value of using edna Great partner to work with, providing excellent customer service when troubleshooting is needed The high fidelity Data Historian component of PreMA provides an excellent basis for continuous validation and expansion of diagnostic and prognostic capability, when sufficient design or operational information is not available during initial system implementation Only data historian in the market that allows receiving and archiving of Complex data (Vibration time waveform, FFT, Diesel Engine Cylinder Firing Pressure Cycle (PV) data set, fluid quality data set) the set of data associated with the captured event is to occupy one data tag value with date/time and qualifier value Allows Archive of Analyzed Data All analysis results performed on-board stored within the data repository containing the raw data for forensic analysis

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