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Predictive Maintenance

How to implement Predictive Maintenance: benefits, techniques and examples

With predictive maintenance (PdM), asset conditions are monitored to prevent failures and anomalies. Types, techniques, and benefits.

We are all aware that there are various types of maintenance, and that, depending on the characteristics of the asset to be maintained, one will choose one rather than another; or we will opt for a combination of more than one type.

Choosing the most suitable maintenance type for managing assets requires a professional facility management software to enhance process productivity, while reducing associated time and costs.

Different types of maintenance subtypes also include the so-called predictive maintenance. But what does it entail, and when and how is it useful? Let’s find out in this article.

What is Predictive Maintenance?

Predictive Maintenance, often denoted by the acronym PdM, is a maintenance technique that monitors equipment performance and conditions. Through data analysis, it identifies potential anomalies or defects to correct them before failures occur.

Introduced in the 1990s, its primary aim is to forecast failures based on specific parameters and factors. Once predictions are made, maintenance personnel can take necessary actions to prevent forecasted failures.

Like any maintenance strategy, predictive maintenance aims to:

  • Reduce failure occurrence, maximizing uptime by improving asset reliability.
  • Optimize operational costs by reducing maintenance work.
  • Cut maintenance costs and maximize production uptime.

Predictive Maintenance vs. Preventive Maintenance: What’s the Difference?

The distinction between preventive maintenance and predictive maintenance lies in the analyzed data.

While predictive maintenance technicians rely on real-time equipment condition monitoring and data analysis, preventive maintenance relies on historical data, averages, and statistical life expectancy to predict maintenance needs.

The Benefits of Predictive Maintenance

Predictive maintenance offers significant advantages over other maintenance approaches, contributing significantly to improving operational efficiency and a company’s profitability.

Some key benefits include:

  1. Preventive intervention: Early intervention capability reduces downtime risk. By constantly monitoring asset conditions and detecting early signs of anomalies or degradation, maintenance interventions can be planned in advance, reducing costly machine downtimes and enhancing operational availability.
  2. Maximization of asset lifespan: Timely issue identification and correction maximize asset and component lifespans. This means equipment and machinery investments can be fully utilized, reducing premature replacements and optimizing returns on investments.
  3. Maintenance cost reduction: Predictive maintenance reduces costs associated with scheduled preventive maintenance or emergency corrective maintenance. This translates to savings in labor costs, spare parts, and unplanned downtime, improving overall company profitability.
  4. Improved workplace safety: By identifying and correcting potential issues before they occur, predictive maintenance contributes to improved workplace safety. Reduced risk of sudden equipment failures or malfunctions creates a safer working environment for operators and technical staff.
  5. Enhanced planning and scheduling: Predictive maintenance, based on data analysis and failure prediction, allows for more effective planning and scheduling of maintenance activities. This optimizes available resources, schedules interventions during low-production impact periods, and minimizes unforeseen downtime.

All these benefits result in significant cost savings by reducing:

  • Maintenance time.
  • Production hours spent on maintenance.
  • Spare parts and supplies costs.

Predictive maintenance programs have been shown to increase return on investment (ROI) by tenfold, thanks to reductions in:

  • Maintenance costs by 25-30%.
  • Failures by 70-75%.
  • Downtimes by 35%-45%.

Types and Techniques of Predictive Maintenance

When discussing “types of predictive maintenance,” we refer to the sectors where this maintenance type applies and the techniques used for each application field. There isn’t a single technology within this maintenance approach; instead, numerous control devices are used related to the technical aspect to be monitored.

Primary predictive maintenance techniques, corresponding to predictive maintenance types, include:

  • Infrared thermography: Infrared thermal analysis is widespread and versatile as it allows non-invasive analysis. It can detect temperatures higher than normal, indicating malfunction or leakage.
  • Sonic and ultrasonic acoustic monitoring: Through acoustic monitoring, maintenance personnel can detect various malfunctions such as gas, liquid emissions, or air leaks. Sonic technology is more economical, while ultrasonic technology, more expensive, is used for mechanical and electrical equipment emitting subtler sounds.
  • Vibration analysis: Particularly useful on machinery emitting specific vibrations according to component operation. As components wear, vibration changes, revealing a new vibration pattern. With constant monitoring, qualified technicians can compare vibration model readings, identifying worn or malfunctioning components in advance, avoiding failures.
  • Oil analysis: Oil analysis is considered highly effective in predictive maintenance. By checking machinery and equipment oil conditions, technicians can determine contaminant presence, viscosity, water presence, etc. The main advantage of oil analysis is that initial test results serve as a baseline for any new machinery and maintenance.
The various types of predictive maintenance

The different types of predictive maintenance

Predictive Maintenance Technologies

Predictive maintenance is supported by a range of advanced technologies enabling real-time monitoring, analysis, and asset state prediction. These technologies are crucial for identifying early problem signals and planning timely maintenance interventions.

Let’s examine some key technologies integral to predictive maintenance.

IoT Predictive Maintenance: What Is It?

The Internet of Things (IoT) is a key technology enabling predictive maintenance by facilitating device, sensor, and management system connection and communication.

With IoT, real-time data on asset operational conditions can be collected and transmitted to centralized systems for analysis and assessment. This connectivity allows continuous remote equipment monitoring, enabling predictive maintenance interventions based on actual conditions rather than predefined maintenance schedules.

Machine Learning for Predictive Maintenance

Today’s context is rich in data, and often vast operational data streams remain underutilized. This is where Machine Learning comes in to transform this dormant data into useful insights into equipment health and performance.

Machine learning plays a crucial role in predictive maintenance, allowing systems to learn from historical and real-time data to identify significant patterns and trends. Using machine learning algorithms, large volumes of data from sensors and monitoring devices can be analyzed to detect anomalies and predict imminent failures.

The most compelling aspect of machine learning in predictive maintenance is its adaptability. These predictive models can be trained to adapt to changes in operational conditions and continuously improve system performance.

AI for Predictive Maintenance

Artificial Intelligence (AI) is employed in predictive maintenance to enhance data analysis and fault prediction accuracy.

Through advanced machine learning algorithms and artificial neural networks, AI can identify complex patterns in data and provide more accurate predictions about asset status and likelihood of future failures. Furthermore, AI can be used to optimize maintenance strategies, suggesting priority interventions and optimizing available resources.

The Role of Cloud and Digital Twins in Predictive Maintenance

In predictive maintenance, digital twins and cloud computing play a crucial role by enabling data storage, processing, and analysis on scalable, flexible platforms.

The cloud offers the ability to store large volumes of data from sensors and monitoring devices securely and accessibly from any location.

Digital twins integrated with IoT technologies are virtual models of physical assets that allow simulating and monitoring real-time asset behavior.

Integration between the cloud and digital twins enables the creation of highly efficient predictive maintenance environments based on data, improving the ability to anticipate and prevent failures and optimizing overall maintenance operations.

Implementing a Predictive Maintenance Program: Best Practices

Clearly, every asset requires a tailored maintenance program, but here are some best practices:

  • Check budget availability: Before approving the maintenance program, ensure financial feasibility. Interact with financial professionals and clients to verify program economic viability.
  • Identify critical assets: Identify resources needing predictive maintenance due to higher costs or delicacy.
  • Create a database: Historical data can be useful for predictive analysis. These data can establish potential failure modes for specific resources and can be useful in developing initial predictive algorithms.
  • Analyze and establish failure modes: Once critical resources are identified, analyze them, and identify their most common and likely failure modes to act preemptively.
  • Implement sensors and monitoring devices: After identifying potential failure modes, take preventive action. Select suitable devices and technologies, among those seen earlier, and other solutions from the constantly advancing technology market. Among the most advanced solutions is IoT technology, facilitating machine-to-machine communication, software solutions, and cloud technology, helping collect and analyze vast amounts of data.
  • Develop predictive algorithms: In this phase, specialized technicians will develop predictive maintenance algorithms based on sensor measurements and other useful data.
Predictive Maintenance Workflow

Predictive Maintenance Workflow

Maintenance of assets and their components is among the most challenging, lengthy, and sometimes costly phases.

Certainly, using professional software is always useful, indeed essential, to ensure that maintenance work yields efficient results. Therefore, I recommend trying out the facility management software yourself, allowing you to plan maintenance, track activities, and manage issues on a single platform.