Why We Need Predictive Maintenance in Industry 4.0 & How Does It Work

Maintenance and Repair Operations (MROs) are vital for the proper functioning of enterprise assets while being key to the continuity and effectiveness of business operations. MROs involve a wide range of activities, like:

• inspecting industrial installations
• performing functional checks on devices and equipment
• replacing damaged or malfunctioning parts
• repairing machinery
• performing various field service engineering tasks.

The planning and execution of these activities require significant human resources and represent a considerable line in the budget of industrial organizations. This is why enterprises are constantly seeking ways to improve the efficiency and cost of their maintenance activities.

In this article, you will learn more about:

• Preventive Maintenance vs. Predictive Maintenance - What is The Difference
• Predictive Maintenance - An Industry 4.0 Application
• The Business Case For Predictive Maintenance Strategy - Applications, Tools, Techniques & Intelligent Asset Management

Preventive Maintenance vs. Predictive Maintenance - What is The Difference

From Reactive Maintenance to Preventive Maintenance

In the past, enterprises maintained their assets reactively. Business repaired or replaced an asset after it had broken down. Accordingly, the asset was restored to its original condition. Reactive maintenance was problematic because it is associated with equipment breakdowns, which stop activities and lead to huge losses.

To mitigate the inefficiencies of reactive maintenance, industrial organizations transitioned to the preventive maintenance model. Preventive maintenance schedules repair and service operations at regular intervals to prevent equipment failures. In other words, preventive maintenance considers the expected lifetime of the assets to inspect and maintain them proactively. In this way, it alleviates unexpected downtime and boosts the continuity of business operations.

What Is Predictive Maintenance Strategy

Preventive maintenance is currently the dominant enterprise maintenance approach. Nevertheless, it is far from optimal, as it typically maintains assets earlier than their end of life (EoL). Hence, it leads to a sub-optimal Overall Equipment Efficiency (OEE). To achieve optimal OEE, enterprises must plan maintenance activities based on factual information about the status of the assets, rather than based on hypothetical EoL values.

This is where predictive maintenance comes in. Predictive maintenance means businesses can schedule maintenance activities based on accurate predictions about an asset’s lifetime.

Predictive maintenance offers many business benefits when compared to the conventional reactive and preventive models:

• Improved Asset Utilization and OEE: With predictive maintenance, enterprises make the best possible use of their assets and improve their OEE.
• Avoidance of Unscheduled Downtimes: Predictive maintenance provides visibility on the actual condition of the assets, which minimizes possible unscheduled downtimes.
• Optimal Planning of Maintenance Activities: Information about the asset’s conditions and their anticipated EoL can be combined with insights on business operations (e.g., production schedules, demand forecasts) towards maximizing revenues and minimizing MRO costs.

Predictive Maintenance - An Industry 4.0 Application

The benefits of predictive maintenance translate to significant cost savings and increased revenue for business enterprises that manage large installations. Nevertheless, predictive maintenance is still not widely deployed, given that getting access to timely and detailed information about the assets’ condition is quite challenging.

In recent years, this is gradually changing thanks to the proliferating deployment of sensing systems and advanced digital technologies (e.g., Internet of Things, BigData, Artificial Intelligence) in the production facilities of large organizations. The deployment of these technologies form parts of the so-called fourth industrial revolution (Industry 4.0).

Industry 4.0 is about deploying sensors and cyber-physical systems to digitize physical processes and implement IT-based data-driven automation and control operations. Industry 4.0 enables a rich set of industrial applications such as flexible automation, predictive maintenance, digital twins, and various supply chain management optimizations.

In the area of enterprise maintenance, Industry 4.0 facilitates the collection of large volumes of digital data about the condition of machinery and equipment. This data collection is empowered by the deployment of different types of sensors, like vibration sensors, acoustic sensors, temperature sensors, power consumption sensors, and thermal cameras.

For example, Disruptive Technologies (DT) temperature sensors can be deployed within critical machine components to identify remarkable changes in the machine’s condition. Likewise, DT’s humidity sensors can be deployed to detect excess moisture, which could indicate corrosion or other issues that can trigger maintenance tasks.

The Business Case For Predictive Maintenance Strategy - Applications, Tools, Techniques & Intelligent Asset Management

Overall, predictive maintenance applications are empowered by modern networks of sensors, which collect large amounts of data about asset conditions. Industrial organizations deploy complex sensor data collection infrastructures consisting of wired or wireless sensors. These infrastructures are able to cope with the different rates and formats of the data streams of the various sensors.

In most cases, data from sensor networks are usually persisted and managed within scalable cloud computing infrastructures. The latter enables the deployment of Big Data infrastructures that can handle very large volumes of heterogeneous datasets, including data with very high ingestion rates.

The availability of rich sets of digital data for the condition of the assets provides a sound basis for determining key maintenance parameters such as an asset’s Remaining Useful Life (RUL). To this end, maintenance datasets are processed by modern Machine Learning and Artificial Intelligence techniques such as Deep Learning. This leads to the extraction of insights about asset conditions and the parameter calculations like EoL and RUL.

Predictive Maintenance Tools & Techniques

Insights about the assets’ conditions are usually visualized in dashboards and presented to maintenance personnel like maintenance engineers and technicians. A typical visualization solution for predictive maintenance presents information about the health and condition of each asset, including information that instructs maintenance professionals on how to best schedule maintenance activities.

In practice, maintenance engineers and service technicians use the information visualized in dashboards as part of their maintenance planning and decision-making methodologies, like:

• Failure Mode, Effects & Criticality Analysis (FMECA)
• Reliability Centered Maintenance (RCM).

Predictive Maintenance Applications

Asset health information can be leveraged by IT-enabled predictive maintenance applications to enable a range of intelligent applications such as:

• Intelligent Control for Optimizing Asset Utilization
• Maintenance Schedules Optimization
• Simulating the Future Condition of the Asset
• Root Cause Analysis
• Leveraging Augmented Reality for Remote Maintenance:

Intelligent Control for Optimizing Asset Utilization

Maintenance insights can be used to drive automation and control functions to optimize asset utilization. For example, upon the detection of early signs of asset degradation, a different operational mode can be activated to avoid stopping operations and prolonging the asset’s Remaining Useful Life (RUL).

Maintenance Schedules Optimization

This application combines maintenance information about individual assets (e.g., a machinery’s RUL) with insights about business processes (e.g., production schedules, customer orders) towards producing optimized maintenance schedules. These schedules indicate the best point to perform asset maintenance considering not only how to maximize OEE and the utilization of the asset, but also how to maximize revenue as well.

Simulating the Future Condition of the Asset

Predictive maintenance parameters can be integrated with powerful digital twins applications to simulate the asset’s future behavior. Likewise, what-if scenarios regarding asset maintenance and operation can be simulated. These simulations can help maintenance professionals evaluate alternative maintenance options and their impact on business operations.

Root Cause Analysis

It is possible to track the health status of assets over time to identify possible causes of their performance degradation. The identification of such causes helps enterprises improve the ways they use and maintain their assets. In several cases, Artificial Intelligence (AI) algorithms can identify hidden patterns of degradation beyond existing domain knowledge. This discovers the root causes of failures and identifies proper remedial actions.

Leveraging Augmented Reality for Remote Maintenance

The extraction of predictive maintenance insights on assets can also give rise to the planning of remote maintenance processes. Augmented Reality visualizations are used to indicate to technicians how to maintain specific assets (e.g., how to repair complex pieces of equipment). Relevant instructions for equipment maintenance are superimposed over cyber-representations of the assets and presented to technicians and field engineers.

Remote maintenance facilitates Original Equipment Manufacturers (OEMs) to support their customers without having to travel on-site. Thus, it saves engineering resources and travel costs for OEMs, while ensuring faster maintenance and repair processes.

Wrapping Up Predictive Maintenance

The extraction of predictive asset insights enables the development of many value-added functionalities that improve maintenance processes and help professionals optimize their decisions regarding assets inspections, equipment repairs, and field service engineering activities.

The rise of Industry 4.0 leads to a proliferation of such applications in settings like smart buildings, production plants, manufacturing floors, and oil refineries. Overall, predictive maintenance is considered one of the “killer” applications of Industry 4.0 for a variety of reasons:

• It is applicable to most organizations, including those that manage industrial, business, governmental and residential installations in many sectors like manufacturing, energy, oil & gas, mining, construction, smart buildings, and smart cities.
• It can be effectively combined with other Industry 4.0 applications like digital automation and digital twins to enable value-added enterprise maintenance functionalities (e.g. remote maintenance and optimal maintenance planning).
• It can be applied to almost all enterprise assets, provided that they are equipped with proper sensors.