Reliability Maintenance Engineering 1 - 3 Data and Decisions
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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.
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The document discusses the stages of the system development life cycle (SDLC), including feasibility studies, system analysis, systems design, development, implementation, and maintenance. It provides details on the objectives and processes involved in each stage, such as defining requirements, designing system components, acquiring or developing software, testing the system, training users, and periodically evaluating systems once implemented.
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Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
AI 101: An Introduction to the Basics and Impact of Artificial Intelligence
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
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Reliability Maintenance Engineering 1 - 3 Data and Decisions
- 2. COLLECTING RELIABILITY DATA TO SUPPORT EFFECTIVE DECISIONS Day 1 Session 3
- 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
- 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
- 13. Equipment Hierarchy • Equipment Class
- 14. Equipment Hierarchy • Equipment Class • Equipment Unit
- 15. Equipment Hierarchy • Equipment Class • Equipment Unit • Subunit
- 16. Equipment Hierarchy • Equipment Class • Equipment Unit • Subunit • Maintainable Level
- 18. Equipment Failure Codes Create short list and codes as needed Three types • Desired function is not obtained • Deviation outside limits • Failure indication observed
- 23. Failure modes
- 26. Data Collection Techniques • Manual • Inspection • Automatic • Surrogate
- 27. Equipment Data
- 28. Failure Data
- 29. Maintenance Data
- 31. Example
- 32. Bottling Plant Example • Data Collected
- 33. Bottling Plant Example • Objective • Improve change overs so they start with higher availability 0 0.25 0.5 0.75 1 1 2 3 4 5 6 7 8 CummulativeAvailability
- 35. Bottling Plant Example • Analysis
- 36. Bottling Plant Example • Options to consider • FMEA • Detailed RCA on top early issues • Redesign of change over process
- 37. Bottling Plant Example • Results • Avoided adding second line • Improved availability • Improved tracking • Improved RCA and redesign process
- 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
Editor's Notes
- 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
- Balance between investment and value
- Gathering and analysis of basic reliability data
- Balance between investment and value
- Balance between investment and value