In recent years, technology innovations in the fields of machine learning and data analytics have opened up new opportunities to optimise maintenance practices.
Organisations see the value in moving from a risk-averse, calendar-based maintenance strategy to a risk-managed, condition-based preventative maintenance strategy for their critical assets.
Organisations also see the potential in reducing the need for manual inspections by introducing electronic inspections i.e. inspections that are performed remotely without removing the asset from operations.
Reliability and maintenance engineers in your organisation likely have already identified the changes to their maintenance practices they would like to implement and know the potential value. The real challenge is turning these great ideas into reality.
A number of organisational and technical elements must be in place for the value to be realised. We assist organisations to work through these implementation issues.
Increasing maintenance intervals
Reducing diagnosis time
Reducing the incidence of unplanned maintenance
Reducing the need for manual inspections
More reliable assets for operations
Improving asset life, reducing, sustaining and replacement
Many organisations have a desire and intent to move to condition-based predictive maintenance practices. The potential value of this move may be known, however, the organisation does not have a clear vision for the new way of operating and does not have a clear plan on how to transition to the new model, including the implications for work practices and technology.
Calendar-based maintenance is wasteful, but it is simple. Moving to condition-based strategies requires an uplift in planning processes. It requires a stronger operating discipline and tighter integration between maintenance and operations planning teams. These areas are problematic for many organisations.
Increasing maintenance intervals involves some element of risk. Risk-based decisions rely on sound data. Some organisations do not have the field data capture devices in place, or the devices are not calibrated, or the data is stranded i.e. not available for use in a timely fashion. It is often a requirement to integrate a number of data and process systems across the O/T and business landscape. Organisations tend to underestimate the extent and complexity of this data integration program.
To be useful for maintenance purposes the condition-data must be organised into an asset-centric data model. This means that all the relevant data for a particular asset is brought together for analysis and automatically matches against the baseline performance model to highlight anomalies. Many organisations do not have an asset-centric data model.
Organisations tend to focus on the technical/engineering aspects of the solution e.g. electronic data capture. However, the value of predictive-maintenance strategies will only be realised if work practices are changed. Changing work practices is an organisational issue not a technical issue.
ATI has developed services to help our clients realise the benefits of Maintenance Optimisation quickly and efficiently. We have developed our Maintenance Optimisation services as a result of a number of engagements involved with transitioning our client’s capability to a more efficient model.
Concept of Maintenance Development
Data Management & Systems Integration
Operational Readiness & Transition Support
Maintenance Opportunities Scan