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