We look at how Machine Learning (ML) is helping to disrupt traditional predictive maintenance models, driving new levels of equipment productivity and performance across asset-centric organisations.
Machine Learning (ML) Predictive Maintenance
Unforeseen equipment downtime represents an increasingly high operational risk to asset-centric organisations, requiring deeper insights into asset health that IoT, and traditional predictive maintenance can facilitate.
Against this backdrop, we’ve seen traditional Computerised Maintenance Management Systems (CMMS) make way for Enterprise Asset Management (EAM), Asset Performance Management (APM), and more recently, an entirely new generation of predictive maintenance-focused tools. It’s fair to say that asset reliability and performance have well and truly made their way up the agenda.
However, such is the pace of change, that these predictive maintenance tools already fall short of delivering the granularity and performance requirements of today’s businesses. Disrupted by the convergence of IoT and Cloud, which together, deliver more comprehensive and real-time data acquisition, Machine Learning Predictive Maintenance can identify potential equipment failure long before it might otherwise raise a flag. Similarly, it can extend the remaining useful life (RUL) of assets through dedicated monitoring and maintenance, rather than sticking to a rigid timeframe based on typical usage.
Legacy Predictive Maintenance versus Machine Learning Predictive Maintenance
Traditional predictive maintenance machine learning models are based on feature engineering. These models are created manually based on experience, expertise and standard metrics and methods. While this approach can be hugely effective, particularly in manufacturing operations, models are specific to a machine within an organisation, therefore become redundant once that machine is replaced. Through applying machine learning at scale, networks can automatically extract the right features from the data, identifying the most common failure patterns, and eliminating a need for manually recreating a model each time a new asset is introduced.
Machine Learning Predictive Maintenance applies algorithms to learn from historical data, and uses live data to analyze failure patterns. Data is collected over time, across a network of assets across a number of organisations, allowing patterns which predict equipment failures to be detected and deep learning algorithms applied.
Combining real-time data with historical trends and variables such as current environmental factors, machine learning can make decisions on when action needs to be taken and make suggestions of what that action should be. The production team can then take appropriate action without actually requiring an understanding of the necessary algorithms.
Machine Learning Predictive Maintenance allows engineers on the shop floor to get the data they need to prevent a failure from happening, rather than manual collection of key indicators such as temperature and pressure, which not only risk inaccuracies and take time to upload, but present a safety risk to personnel. Strategically, this approach allows engineers to fully focus their skills on where they matter the most: the maintenance plan and assets that need their attention.
Best practice exemplified
Consider a transport company which is informed that one of its buses has broken down. Legacy predictive maintenance might point to the fact that its engine is coming up for a service, therefore the recommended action is to expedite this service and identify the problem. Machine Learning Predictive Maintenance could leverage its wealth of insights to identify that the type of engine used in the bus is subject to a common fault, allowing fast diagnostics, deployment of the right skills, and remedial action to resolve the issue. Not only does this minimise the vehicle’s ‘downtime’, it minimises resource utilisation and contributes to the company’s reputation for reliability.
Overdue disruption; continuous innovation
Machine learning can be used to quickly meet new objectives, whether it’s increasing throughput to accommodate increased demand in a certain area, or to adjust to just in time production in the event of a dip or change in either supply or demand. Crucially, this can be achieved in a switch rather than investment in a data science team, requiring substantial investment and training.
Given the uncertain times we’ve become accustomed to in recent years, it’s no wonder its approach, which is ingrained in using algorithms to find patterns in data to predict future events, has been adopted across a wide range of industries. Machine Learning Predictive Maintenance has clear reason to disrupt the legacy predictive maintenance sector.
Not only is the global market for machine learning set to grow from $17.1 billion in 2021 to $90.1 billion by 2026, such is the momentum behind this disruption, that by 2026, 60% of IoT enabled predictive maintenance solutions will be delivered as part of enterprise asset management products.
Accessible to all organizations, and with minimal staff resource required to set up or for ongoing monitoring, and the flexibility to scale as required, the only thing standing in the way of achieving impact in this way is an organization’s ambition to move away from traditional manual operations.
Bringing machine learning to a factory unlocks the potential of its assets and its key condition indicators. Machine learning doesn’t have a maximum number of assets to monitor, it is able to manage as little or as much data put into it. And it gets more accurate with its insights as time goes by, as the additional data enriches its modelling and analysis experience. Providing insights, rather than straightforward monitoring of thresholds, the plant engineers can focus where their time is needed the most and be more efficient with unplanned downtime reduction and machinery life extension.
The benefits of this in-depth, knowledge-based approach to asset management extend to all corners of an organization, as the impact empowers and challenges staff to identify and embrace new opportunities.
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