Reliability Maintenance Engineering Day 1 session 3 Data and Decisions
Three day live course focused on reliability engineering for maintenance programs. Introductory material and discussion ranging from basic tools and techniques for data analysis to considerations when building or improving a program.
3. Objectives
• Establish field data collection systems
• Creating taxonomies and equipment failure
codes from ISO 14224
• Gathering and examining basic reliability data
• Developing analysis information for repair
• Determine cost advantages of alternative
action plans to meet requirements
4.
5. Data Quality
• High quality data is
• Compete
• Compliance
– Reliability parameters
– Data types & formats
• Accurate
6. Obtaining Quality Data
Investigate data sources
• Define objective for data collection
• Inventory or operational data complete
• Sufficient and relevant data available
• Identify installation date, population and
operating period(s) for equipment
7. Obtaining Quality Data
Preparation of sources
• Run a pilot of collection
methods
• Plan collection process
– Schedules, milestones, seq
uence and number of
units, time period, etc.
• Training
• Collection process
assurance plan
8. Data Sources
• What do you need to
make decisions?
• Maintenance
Management System
• Prioritize according to
importance to safety
and production
11. Boundary description
• Describe what is and is
not included
Needed for consistent
• Data collection
• Data compilation
• Data Analysis
• Decision making
Show on diagram
• Subunits
• Interfaces to
surroundings
• Be clear what is in and
outside boundary
18. Equipment Failure Codes
Create short list and codes
as needed
Three types
• Desired function is not
obtained
• Deviation outside limits
• Failure indication
observed
39. Summary
• Establish field data
collection systems
• Creating taxonomies and
equipment failure codes
from ISO 14224
• Gathering and examining
basic reliability data
• Developing analysis
information for repair
• Determine cost advantages
of alternative action plans
to meet requirements
Collecting reliability data
to support effective decisions
Notas del editor
Data for reliability analysis
Field data collection system
4.2 Guidance for obtaining quality dataTo obtain high quality data, the following measures shall be emphasized before the data collection process starts:. investigate the data sources to make sure the required inventory data can be found and the operational dataare complete;. define the objective for collecting the data in order to collect relevant data for the intended use. Examples ofanalyses where such data may be used are: Quantitative Risk Analysis (QRA); Reliability, Availability andMaintainability Analysis (RAM); Reliability-Centred Maintenance (RCM); Life Cycle Cost (LCC);. investigate the source(s) of the data to ensure that relevant data of sufficient quality is available;. identify the installation date, population and operating period(s) for the equipment from which data may becollected;. a pilot exercise of the data collection methods and tools (manual, electronic) is recommended to verify thefeasibility of the planned data collection procedures;. prepare a plan for the data collection process, e.g. schedules, milestones, sequence and number of equipmentunits, time periods to be covered, etc.;. train, motivate and organize the data collection personnel;. plan for quality assurance of the data collection process. This shall as a minimum include procedures for qualitycontrol of data and recording and correcting deviations. An example of a checklist is included in Annex C.During and after the data collection exercise, analyse the data to check consistency, reasonable distributions,proper codes and correct interpretations. The quality control process shall be documented. When merging individualdata bases it is imperative that each data record has a unique identification.
Balance between investment and value
Taxonomies and equipment failure codes from ISO 14224 or other system
Drawing of boundary description figure 1 in section 5 of 14224
Full figure 2 of hardware classification and boundary classifications