Dynamic Blackout Probability Monitoring System for Cruise Ship Power Plants
Abstract
:1. Introduction
2. Materials and Methods
2.1. Methodology Overview
2.2. Step 1—Development of Safety Model
2.3. Step 2—Selection of the Monitored Parameters and Reliability Data
- Measured parameters that sufficiently and effectively depict/represent the actual system health based on the pertinent literature.
- Measured parameters that represent the system configuration and power demand, e.g., operating DG set(s).
- Measured parameters monitored by the existing ship alarm and monitoring system.
- Measured parameters from the ship plant critical components, as identified from previous safety analyses or accident investigation data.
2.4. Step 3—Estimation of Failure Rates Using Sensors Measurements
2.5. Step 4—Integration of Sensor Measurements Estimation and Database Data
2.6. Step 5—Dynamic Analysis
2.7. Step 6—Simulation in Virtual Environment
3. Investigated System Description
Case Studies Description
4. Results
4.1. Step 1—The Developed Safety Model
4.2. Step 2—Selection of the Monitored Parameters
4.3. Steps 3−6—Simulation Results
4.4. Discussion
5. Conclusions
- Specific operational parameters as DG load and number of connected DG sets need be used as input into the safety monitoring system, as these influence the system’s probability of failure.
- An operation with a single DG set increases the PoB to the red warning level.
- The PoB during start of DG sets also reaches the red level.
- Failures in operating components can increase the PoB also above the desired threshold, however their criticality is varying in time dependent on the system other parameters.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations/Nomenclature List
Greek symbols | |
Symbol | Explanation |
Weibull shape factor [-] | |
Aggregated failure rate for component estimated using sensor measurements and reliability data | |
Blackout failure rate [h−1] | |
Failure rate for component [h−1] | |
Failure rate for component estimated using sensor measurements [h−1] | |
Repair rate for component [h−1] | |
English symbols | |
Symbol | Explanation |
Feature [variable units] | |
Normal feature value [variable units] | |
Feature degradation slope [variable units/h] | |
Health index for component i | |
Birnbaum’s importance measure [-] | |
An averaged over time metric [-] | |
Fussell-Vesely importance measure [-] | |
An averaged over time metric [-] | |
Maximum Continuous Rating power [kW] | |
Number of simulations [-] | |
Number of criticality assessments implemented [-] | |
Operational parameters [depending on parameter] | |
Operational time [h] | |
Aggregated probability [-] | |
Probability of failure for operating component [-] | |
Probability of failure of safety system [-] | |
Probability of specific system states [-] | |
The probability of failure on demand [-] | |
Probability of top event in specific system configuration [-] | |
Probability of top event [-] | |
Reference probability of top event [-] | |
Number of identical components | |
Time [h] | |
Time of last maintenance [h] | |
Inspection or maintenance interval [h] | |
Weight depicting which information is selected. w = 1, sensors are used to estimate failure rate, w = 0 failure rate from database is used. | |
Subscripts | |
Symbol | Explanation |
Importance measure estimation number in dynamic simulation | |
Component | |
j | Basic event in Fault Tree |
Failure rate estimated based on measurements | |
Abbreviation | Explanation |
BDMP | Boolean logic Driven Markovian Process |
BT | Bow Thruster |
CASA | Combinatorial Approach to Safety Analysis |
DEP | Diesel-electric Propulsion |
DG | Diesel Generator |
ER | Engine Room |
HT | High Temperature |
LT | Low Temperature |
MI | Maintenance Interval |
PM | Propulsion Motors |
PMS | Power Management System |
PoB | Probability of Blackout |
PoDGloss | The probability of sudden loss of a DG set |
TC | Turbocharger |
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Warning Levels | Range |
---|---|
Red | [ × 5, × 50] |
Orange | [ × 0.2, × 5] |
Yellow | [ × 0.02, × 0.5] |
Green | [ × 0.002, × 0.05] |
System Components | Original Fault Tree Probability [30] | Modified Fault Tree Probability Estimation (Used for Simulation) | |
---|---|---|---|
Operating components | Software, hardware, communication and sensors failures following exponential distribution for failure rate [41] | t | |
Other components with preventative maintenance following Weibull distribution for failure rate | |||
Parts with preventive maintenance where a single component failure out of identical will lead to event occurence (based on [41]) | Replaced with OR gates connecting components failures. Each component failure rate is modelled by | ||
Parts with preventive maintenance where all the identical components must fail for event occurrence (based on [41]) | Replaced with AND gates connecting components failures. Each component state is modelled as a Markov process, whereas each component failure rate is estimated by | ||
Safety systems | Tested standby equipment failure on demand (except for software failures) [41] | ||
For safety system/functions with continuous monitoring failure on demand [41] | Modelled as Markov process | ||
Safety functions with periodical testing failure on demand [41] | |||
For software failures in safety functions [41] | |||
Unavailability due to periodical maintenance of standby equipment where components are standby (based on [41]) | Replaced with AND gate connecting components failures. Each component failure rate is estimated by Equation (8) and each component state is modelled as a Markov process. |
Case Study No. | Analysis Conducted | |
---|---|---|
1 | 0 | Estimation of PoB every 0.5 h for 168 h (7 days, 1 week) with horizon prediction ( of 24 h |
2 | 0.5 | Estimation of PoB every 0.5 h for 168 h (7 days, 1 week) with horizon prediction () of 24 h and importance analysis every 24 h |
3 | 1 | Estimation of PoB every 0.5 h for 168 h (7 days, 1 week) with horizon prediction ( of 24 h |
a/a | Component | Normal/Alarm Value | MI * (hours) | |
---|---|---|---|---|
1 | Engine Thrust bearings | Temperature | 80/100 °C | 18,000 |
2 | Engine Main bearings | Temperature | 80/100 °C | 18,000 |
3 | DG engine high temperature cooling water pump | Pressure at engine inlet | 4/2 bar | 10,000 |
4 | DG set engine low temperature cooling water pump | Pressure at engine inlet | 3.6/2 bar | 10,000 |
5 | Engine low temperature cooling water pump | Pressure | 3.6/2 bar | 10,000 |
6 | Cylinders Exhaust gas | Temperature at exhaust gas port | 450/490 °C | 6000 |
7 | Turbocharger (TC) | Temperature at turbine inlet | 450/490 °C | 12,000 |
8 | Engine lubricating oil cooler | Temperature at engine inlet | 70/80 °C | 10,000 |
9 | Lubricating oil pump | Pressure at engine inlet | 4/3 bar | 5000 |
Component or Software Function Failure | Type of Failure | |
---|---|---|
Power Management System failure to reduce load of propulsion motors | 0.0047 | Software |
Arc protection software failure | 1.21 × 10−9 | Software |
Arc in switchboards N1 and N2 | 0.0072 | Physical |
DG 1 water cooler failure | 0.0004 | Physical |
DG 1 Engine lubricating oil cooler | 0.0004 | Physical |
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Bolbot, V.; Theotokatos, G.; Hamann, R.; Psarros, G.; Boulougouris, E. Dynamic Blackout Probability Monitoring System for Cruise Ship Power Plants. Energies 2021, 14, 6598. https://doi.org/10.3390/en14206598
Bolbot V, Theotokatos G, Hamann R, Psarros G, Boulougouris E. Dynamic Blackout Probability Monitoring System for Cruise Ship Power Plants. Energies. 2021; 14(20):6598. https://doi.org/10.3390/en14206598
Chicago/Turabian StyleBolbot, Victor, Gerasimos Theotokatos, Rainer Hamann, George Psarros, and Evangelos Boulougouris. 2021. "Dynamic Blackout Probability Monitoring System for Cruise Ship Power Plants" Energies 14, no. 20: 6598. https://doi.org/10.3390/en14206598
APA StyleBolbot, V., Theotokatos, G., Hamann, R., Psarros, G., & Boulougouris, E. (2021). Dynamic Blackout Probability Monitoring System for Cruise Ship Power Plants. Energies, 14(20), 6598. https://doi.org/10.3390/en14206598