Prediction begins with prevention

For predictive maintenance strategies to bring measurable benefits in the form of cost reduction and optimization of inspection times, it is necessary to implement preventive maintenance.

Many maintenance managers want to implement predictive maintenance (PdM) strategies that enable them to predict failures and identify the optimal time for inspections, which supports cost optimization.

However, few realize that the foundation of effective prediction is well-functioning prevention. Without a solid foundation in the form of preventive maintenance, effective implementation of prediction is not possible.

What steps should be taken to stop reacting (from failure to failure) and switch to a more comprehensive approach, one that focuses on standardized inspections and, subsequently, predictive maintenance?

Here are some useful terms:

• Reactive maintenance
  Repair work undertaken after a breakdown or fault has often been ignored, leading to high costs and extended downtime. The exception to this is when work on replaceable components is planned.
• Preventive maintenance (PM)
  Regular actions based on the cyclical nature of machines, such as inspections and component replacements, should include detailed information about the times and activities performed. Their purpose is to reduce the risk of machine failure.
• Condition-Based Maintenance (CBM)
  Maintenance based on the current technical condition of equipment – monitoring parameters such as temperature, vibration and pressure using sensors enables service decisions to be made based on current data. CBM is a natural progression in the development of PM towards more advanced solutions.
• Predictive Maintenance (PdM)
  The use of historical data, IoT sensors, models and algorithms to predict failures before they occur. Data from earlier stages of prevention and machine condition monitoring (CBM) are of key importance.

Without proper preventive maintenance, the data collected in the predictive system will be disordered, and the algorithms will not have reliable patterns for analysis.

How to implement good prevention?

Implementing effective preventive maintenance requires several key actions:

• machine criticality analysis: identify which machines have the greatest impact on production and start with them.
• developing an inspection schedule: create a maintenance plan based on historical data and manufacturer recommendations.
• Standardize procedures: ensure that each inspection is carried out according to established procedures.
• Train employees: train staff to know how to carry out preventive tasks.
• Monitor and analyze failures: regular analysis of service reports helps to eliminate recurring problems.

Implementation of a CMMS solution

A CMMS (Computerised Maintenance Management System) supports prevention through specific functions that facilitate the standardization of work. These include:

• automatic planning and prioritization of activities based on cyclicality or motor hours, using algorithms that remind technicians of upcoming maintenance and inspection dates.
• recording of service history – detailed documentation of all repairs and inspections, with a mobile viewing option that allows you to check what has and has not been done recently.
• creation of digital checklists – these lists eliminate the need for paper documents and provide support when controlling and auditing access to protocols, including those generated automatically.
• activity time reporting – the ability to track the time spent on inspections allows for process optimization and verification of non-compliance.
• integrating and modifying procedures according to changing processes and the development needs of the organization.

Case Study – CMMS implementation and preventive strategy

Problem: A company operating in the FMCG sector was struggling with high failure rates of key machines and problems with service documentation during audits. In addition, the lack of any standards posed a serious challenge for the maintenance manager.

Actions:

Implementation of a CMMS system and establishment of a service schedule for key machines.

Recording all inspection activities in digital checklists.

Analysis of the time required to perform standard activities during inspections.

Results:

1. Reduction in failure rates – regular inspections reduced the number of unplanned downtimes.
2. Audit facilitation – organized documentation facilitated ISO audits and accelerated access to historical data.
3. Unexpected bonus: data analysis showed that one of the technicians needed significantly more time to perform standard tasks than the others.

How can adding sensors achieve condition-based maintenance?

Adding IoT sensors to machines is another step towards dynamic condition monitoring. They enable real-time tracking of parameters, allowing action to be taken when it is needed. The key elements are:

• Real-time monitoring: sensors record parameters such as temperature, vibration, pressure and energy consumption, enabling rapid detection of irregularities.
• integration with CMMS: the CMMS system collects data from sensors, allowing for the automatic generation of alerts and inspection schedules based on the current condition of the machine.
• precise intervention planning: thanks to data analysis, it is possible to optimize repair and inspection plans based on the first signs of wear and anomalies.

How to get started with predictive maintenance?

Once prevention and condition-based maintenance have been implemented, it is easier to move on to predictive maintenance.

A CMMS system, working in conjunction with sensors, creates a rich historical database. This is the foundation for building predictive models.

Analytical tools allow you to develop algorithms that can predict potential failures based on the collected data.

Prediction tests can be performed on selected areas/equipment from all technical facilities. CMMS facilitates this thanks to available reports, analyses and location divisions.

Prediction is a continuous process requiring constant observation and model updates, which is why human knowledge supported by appropriate tools is still needed.

Case Study – The Road to Prediction

An FMCG company began its transformation by implementing preventive maintenance using a newly implemented CMMS system. Here is their path:

• Introduction of prevention: the company established inspection schedules, recorded breakdowns, parts withdrawals and CMMS qualifications. As a result, the failure rate of key machines decreased and processes were standardized.
• addition of IoT sensors: the next step was to equip the machines with sensors that were integrated with the CMMS system. Real-time monitoring reduced response times in the event of breakdowns and optimized the number of inspections.
• Transition to prediction: currently, it cannot be said that the company has ‘implemented prediction’ because continuous process improvement shows that they are implementing it on an ongoing basis. The CMMS system assists in data analysis, and predictive models and algorithms have turned failures into faults (which do not cause machine unavailability).

Summary

The key to effective maintenance is to gradually build a solid foundation. This involves moving from reactive repairs and unexpected breakdowns, through planned preventive measures, to advanced predictive strategies.

The key to success is a solid foundation in the form of preventive maintenance (PM), which enables the collection of reliable data necessary to implement predictive maintenance (PdM). The CMMS system supports these processes by enabling planning automation, activity documentation and integration with IoT sensors.

Examples show that this approach leads to a significant reduction in breakdowns, facilitates audits and optimizes the work of technical teams.