Design the pilot for sensor analytics for predictive maintenance so its result is decisive rather than merely encouraging.
Industrial Sensor Data Analysis for Predictive Maintenance
Learn to design, measure and defend your organisation's approach to sensor analytics for predictive maintenance.
📍 Abu Dhabi🗓️ 5 training days📚 4 modules🎓 Accredited certificate
5intensive training days
4scientific modules
8training sessions
32detailed points
Course Overview
Vendors sell sensor analytics for predictive maintenance as a product; it behaves in practice as a change programme. The distance between a working prototype of predictive maintenance and a system the business can depend on is where most budgets disappear. The programme uses small-group work so that each participant's treatment of the digital and data-driven work capability is examined, not just described. Applied research in digital and data-driven work consistently shows that early structure around sensor analytics for predictive maintenance reduces downstream rework. It treats this strand of digital and data-driven work as an operating discipline and equips participants to run it as one. The outcome is a practitioner who can hold a position on predictive maintenance and revise it on evidence. It suits anyone whose decisions touch sensor analytics for predictive maintenance, in large organisations and small ones alike. What blocks progress on predictive maintenance is usually unclear ownership rather than unclear intent. Participants leave with a plan for the digital and data-driven work discipline sized to what their organisation can realistically absorb.
Expected Learning Outcomes
Build a register of the risks attaching to predictive maintenance and keep it current.
Design a practical operating method for sensor analytics for predictive maintenance that fits the organisation's size and maturity.
Evaluate the bias, fairness and explainability obligations attaching to predictive maintenance.
Specify the data sensor analytics for predictive maintenance depends on, where it originates and who is accountable for its quality.
Plan the handover of predictive maintenance so that capability is not lost when key staff move on.
Establish monitoring that detects model or service degradation in sensor analytics for predictive maintenance before users report it.
Who Should Attend
Operations managers whose processes are changed by sensor analytics for predictive maintenance.
Product owners prioritising the roadmap for predictive maintenance.
Officers preparing reports on sensor analytics for predictive maintenance for boards or oversight committees.
Data and analytics leads responsible for the pipelines behind predictive maintenance.
Information security officers reviewing the exposure created by sensor analytics for predictive maintenance.
Procurement and contracting staff whose agreements set obligations around predictive maintenance.
Course Modules
Sensor analytics for predictive maintenance: cost, licensing and total running expense
2 sessions · 8 pointsSession 1Proving sensor analytics for predictive maintenance paid for itself
- Test the procedure for sensor analytics for predictive maintenance against a realistic scenario.
- Estimate compute and licensing cost for predictive maintenance at expected and at peak load.
- Set the metrics that will show whether sensor analytics for predictive maintenance is drifting from its intended behaviour.
- Confirm the retention and deletion rules applied to data inside predictive maintenance.
Session 2The governance predictive maintenance needs and the governance it does not
- Compare the cost of sensor analytics for predictive maintenance with the cost of its absence.
- Agree who is on call for predictive maintenance outside working hours.
- Define the trigger that would require sensor analytics for predictive maintenance to be redesigned.
- Identify every system predictive maintenance must read from or write to.
Predictive maintenance: governance, ethics and explainability
2 sessions · 8 pointsSession 1Building the method for predictive maintenance step by step
- Decide which legacy process sensor analytics for predictive maintenance retires, and set the date.
- Identify single points of dependency in predictive maintenance and reduce them.
- Record what was learned when sensor analytics for predictive maintenance did not go as planned.
- Specify the fallback path when predictive maintenance is unavailable.
Session 2Where predictive maintenance touches systems nobody wants to change
- Rank the weaknesses in sensor analytics for predictive maintenance by consequence rather than by ease of fixing.
- Distinguish symptoms from causes when predictive maintenance underperforms.
- Define the exit route from the supplier supporting sensor analytics for predictive maintenance.
- Classify the data in predictive maintenance and apply access controls that match the classification.
Predictive maintenance: architecture, integration and the existing estate
2 sessions · 8 pointsSession 1The hard cases in predictive maintenance and how to reason about them
- Prepare the response for the most likely failure in sensor analytics for predictive maintenance.
- Confirm that contractual obligations around predictive maintenance are understood.
- Record the reasoning behind each architectural choice in sensor analytics for predictive maintenance.
- Benchmark the organisation's predictive maintenance against comparable operations.
Session 2What breaks first when sensor analytics for predictive maintenance meets real volume
- Define the service level sensor analytics for predictive maintenance must meet and what happens when it is missed.
- Assess the regulatory obligations predictive maintenance triggers in each jurisdiction.
- Measure the current quality of the data feeding sensor analytics for predictive maintenance before assuming it is usable.
- Identify the skills the team lacks to operate predictive maintenance independently.
Predictive maintenance: business case, scope and the data it depends on
2 sessions · 8 pointsSession 1Getting other functions to support predictive maintenance
- Establish who is informed, consulted and accountable in sensor analytics for predictive maintenance.
- Verify that predictive maintenance still performs when input volume doubles unexpectedly.
- Establish the boundary of sensor analytics for predictive maintenance and record what sits outside it.
- Test predictive maintenance against edge cases drawn from real historical records.
Session 2Explaining predictive maintenance to people whose jobs it changes
- Set out how exceptions to sensor analytics for predictive maintenance are requested and approved.
- Collect evidence on the present handling of predictive maintenance before proposing changes.
- Set out the decisions in sensor analytics for predictive maintenance that require sign-off and by whom.
- Plan the sequence in which improvements to predictive maintenance will be introduced.
Choose the package that suits you
Silver Package
At least 3 people
USD1,250
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Gold Package
At least 3 people
USD1,850
- 5-night stay in a 5-star hotel
- Workshop or Program Participation
- Airport Transfers
- Customized Badge
- Expert Mentorship (Private Sessions)
- Supervision & Secretarial Services
- Accredited Certificate of Participation
- Complete Training Kit
- Coffee Break
- Closing Ceremony
Complete your registration
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