Is your predictive maintenance programme as good as it should be?

Is your predictive maintenance programme as good as it should be? Predictive maintenance is a constantly evolving strategy, with new technologies and integration tools constantly looking for a way to position themselves in the various branches of predictive maintenance.

It is true that right now some maintenance managers have some kind of predictive maintenance programme in their plant, although it is true that the application of predictive maintenance in the plant is not always easy. predictive maintenance tools vary considerably from plant to plant. Many tell us that their predictive maintenance programmes The surprising thing is that there is still a group of maintenance engineers, responsible for major industrial facilities, who do not currently plan to incorporate predictive tools in their plant.

A survey conducted by Plant Services to its readers, mainly industrial maintenance managers and managers, notes that more than half of its respondents (57%) said that maintenance managers are involved in predictive maintenance decisions, while 48% said that plant managers are involved.

In that survey, only 21% said that corporate executives are responsible for decision making in the predictive maintenance programme, possibly suggesting a lack of commitment to what happens on a daily basis on the shop floor or to the data generated through the predictive maintenance programme.

When asked which of the predictive maintenance technologies have developed in their plants, showed that the predictive technologies infrared, lubricant analysis, and vibration analysis top the list. This is not surprising, given the attention these technologies have received in recent years and the high return on investment they offer.

Predictive modelling software is on the radar of maintenance managers; a quarter of readers plan to implement it in their plants within the next three years.

When considering motivations for implementing predictive maintenance technologies, the top factors, according to respondents, included:

• Improving uptime,
• Reduction of operational costs, and
• Reduced maintenance costs, less popular were knowledge transfer and energy management.

Getting the data right is a key factor in a predictive maintenance programme.

The success of a predictive maintenance programme depends to a large extent on collecting the right data. The readers of Plant Services report that they use various methods of data collection for your predictive maintenance programme. Despite the increase in automated data collection, paper-based data collection is still the most prevalent.

This does not mean that maintenance professionals are not interested in an interconnected plant. As plants strive for a more connected environment, the integration of predictive maintenance systems The high level of high-level training is becoming an increasing priority.

Historian systems and EAM/CMMS systems top the list of predictive maintenance solutions The survey also found that the plants are implementing high-level reliability solutions, with several respondents planning to integrate reliability solutions within the next three years.

In addition, many respondents plan, within the next three years, to deploy industrial tablets and smartphones in their plants. But for others, the adoption of advanced data tools seems to continue to lag behind their arrival and proliferation in the market.

While requests for predictive maintenance tools are large, most plants do not have the capacity or the financial luxury to manage all their assets with predictive technologies. The survey found that, in terms of production and assets, such as rotating equipment and pumps, were more likely to be covered through a predictive maintenance programme. 

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