Switching to industrial predictive maintenance can improve OEE

Technology is changing the day-to-day operation of companies that focus on cost-effective automation processes, improving product quality and simplifying equipment maintenance. This technological change has enabled manufacturers to shift from a reactive strategy of operating to failure to a reactive approach. industrial predictive maintenance This results in a significant improvement in overall equipment effectiveness (OEE).

The impact of industrial predictive maintenance on OEE

The industrial predictive maintenance is a data-driven maintenance strategy that uses sensors to continuously monitor the condition and performance of assets. It runs that data through a set of pre-set predictive algorithms to estimate when a given asset should fail, allowing maintenance teams to schedule preventive work just before failures occur, shortening and minimising the number of both scheduled and unscheduled downtime in the process.

The industrial predictive maintenance enables companies to eliminate unnecessary breakdowns or production stoppages that are common with reactive maintenance strategies. It ensures that all production assets will be available throughout production cycles with minimal interference to production schedules. Increased availability of production assets significantly improves overall equipment effectiveness (OEE) for all production processes. Learn about some of the OEE management strategies in industrial plants.

The implementation of a programme of industrial predictive maintenance ensures that the performance of all assets in a production facility is maintained at optimum levels, reducing yield loss and improving profits. See the 8 financial reasons to implement predictive maintenance technology. 

Unprecedented equipment failures and reliance on worn, misaligned, defective or obsolete parts are also minimised. This results in improved quality of the final product. In other words, the number of defects is minimised, as quality losses due to machine problems are greatly reduced.

Steps to move from reactive to predictive industrial maintenance

In addition to improving the OEE value of a production facility, companies can gain many additional benefits by implementing an OEE system. industrial predictive maintenance. Some of the notable impacts of PdM are improved operational safety, extended life of critical assets, increased profitability and improved customer satisfaction.

The following is an outline of how to establish the transition from reactive to reactive maintenance. industrial predictive maintenance. Consult the comparison between proactive and reactive maintenance to know their differences.

1. Establish an implementation strategy

   An appropriate implementation strategy must be developed prior to the transition from a reactive to a predictive programme. The initial phase consists of a rigorous audit of all production assets to identify and prioritise the equipment that should be included in the industrial predictive maintenance. Priority is given to assets with a history of frequent failures, critical assets that operate 24 hours a day, as well as assets that are expensive to replace and difficult to access.

   Once the assets to be included in the programme have been identified, past maintenance data should be examined to create a preliminary predictive model and perform a detailed failure mode and effect analysis (FMEA) on the assets.

   After conducting a detailed FMEA, a list of high-priority, high-risk equipment is generated. The list is then used to create and refine a pilot programme by setting milestones, devising assessment methodologies, creating OEE improvement targets and defining data acquisition approaches.

2. Deploy additional infrastructure

   A lot of data must be collected and fed into the predictive algorithms so that accurate predictions can be made. To do this, several condition monitoring sensors have to be installed in the pilot equipment. These sensors collect real-time data and transfer the information via dedicated IoT networks to a centralised database for analysis. They can be used to measure a variety of different signals, from electrical currents and vibrations to noise levels and corrosion.

   From this and previous maintenance data, a suitable prediction algorithm is developed. Advanced systems use AI and machine learning technologies in this phase. The algorithms check the supplied sensor data against the pre-set conditions and generate alerts when deviations are detected.

   To facilitate communication between machines and maintenance teams, a user-friendly control panel is required. It is essential that the data collected is protected against misuse or external attacks that could impede production cycles.

3. Training, testing and collecting feedback

   Adopt a programme of industrial predictive maintenance implies that several maintenance activities undergo significant changes. For example, some manual readings are eliminated and new technologies, tools and maintenance procedures are introduced to comply with the new procedures. The organisation also needs to implement a CMMS that supports the industrial predictive maintenance or comparable software, if you do not already use one.

   While the pilot programme is being tested, it is essential that all maintenance staff receive adequate training on the newly implemented technology. During this time, they are briefed on the changes to their roles and taught how to navigate the new systems.

   Maintenance teams will subject pilot equipment to various production schedules and monitor its response to changes. As predictive algorithms receive more data, their prediction will become increasingly accurate.

   In this phase, the management receives feedback from the implementation teams to assess the user-friendliness of the industrial predictive maintenance and its contribution to OEE improvement. Based on team performance data and personal feedback, the company can develop a robust response procedure to address alerts generated by the programme.

4. Improve and expand the industrial predictive maintenance programme.

   It takes time for an organisation to fully test and verify the contribution of a industrial predictive maintenance to the production schedule and performance. The pilot programme is subjected to a variety of operational scenarios in an effort to extract a large amount of actionable data. The pilot phase provides valuable information to enable the company to expand or refine the industrial predictive maintenance.

   Gaps identified during the testing phase are rectified by refining the infrastructure or predictive algorithms. Once all issues are resolved, the company can proceed with a gradual and large-scale roll-out of the programme. The scale-up of the programme should be implemented in stages and in a way that avoids overloading existing resources.

   The programmes of industrial predictive maintenance require continuous upgrades throughout their lifetime to remain relevant and competitive. Upgrades are vital to improve data security, increase the speed of data acquisition and analysis, and streamline communications between human and machine interfaces.

Key areas for industrial predictive maintenance to focus on

• Real-time status monitoring

  The industrial predictive maintenance requires a set of tools to function that provides you with the necessary data in real time. Investing in construction equipment tracking software, while it may require a significant investment, is a big step if you want to make the transition to predictive maintenance as seamless as possible.

  A good solution provides insight into equipment running costs on an hourly basis, but can also help ensure that high value assets are monitored in detail. In addition, smaller items such as hand tools can also be allocated to different categories, as they have a lower value and may not need constant monitoring.

• Including the right people in data analysis

  All relevant departments should have access to all relevant data collected by the monitoring system to ensure a satisfactory data assessment.

  The industrial predictive maintenance should not enhance monitoring efforts. To ensure that the data collected is used efficiently, there should be a clear structure in the company as to who monitors specific elements and how the data should be transmitted.

• Establishing criteria for continued success

  It is necessary to identify the criteria to be achieved in a specific time frame to obtain ROI. Having a clear objective that governs ROI ensures the ability to measure the effectiveness of the industrial predictive maintenance from the day of implementation.

• An asset management programme to keep everything under control

  In order for a programme to industrial predictive maintenance to be successful, it is necessary to ensure the participation of all key players in the organisation. That's why construction management software is essential to ensure that the company is set up for success. With integrated construction software, you can integrate work order and equipment data, providing you with the most accurate information on the status of an asset, plus work order history, maintenance information and more.

Having constant oversight of teams, the right people involved in data evaluation, the right success criteria and integrated construction management software allows you to proactively maintain and improve your construction management strategy. industrial predictive maintenance of equipment.

Final conclusions of the industrial predictive maintenance

The programmes of industrial predictive maintenance are cost-effective solutions in the long term, especially considering the OEE improvements they can bring. The implementation of a industrial predictive maintenance should be carefully thought through to avoid the most common mistakes.

Transition requires proper planning and strategic execution to avoid production losses. Initial investment costs can be high and some processes require expert intervention. Despite the challenges, the selection of the right model of industrial predictive maintenance has the potential to turn manufacturing companies into smarter and more profitable systems.

Find out more about how to organise a predictive maintenance programme that works.