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As Industry 4.0 continues to generate analyst and media attention, many facility owners are struggling with the realities of implementation. The potential ROI from increased uptime and new revenue streams are compelling, but there is no single roadmap for how to realize the benefits of IIoT.
This document reviews the subject of Machine Learning for Asset Maintenance and the role it plays in Industry 4.0.

Falling IT Infrastructure Costs: the Enabler of Industry 4.0
Industry 4.0 (or Industrie 4.0) started as a German government-backed initiative that supported decentralized “smart” manufacturing. Revolutions do not occur in a vacuum and in the case of Industry 4.0, there were several factors:
First, the cost of computing power and data storage has fallen dramatically. For example, in 1964 the cost to store a terabyte of data would cost the equivalent of $3.5 billion dollars. Today, the cost to store the data is approximately $25. Data production has soared. PwC forecasts that by 2020 data production will be 44 times greater than 2009 levels. Similar trends are prevalent in cloud computing, connectivity and other infrastructure that form the basis of the Smart Factory.

A second factor that is enabling Industry 4.0 is the cost of sensor data. According to Goldman Sachs / BI Intelligence, the cost of IoT sensors is expected to fall from $1.30 in 2004 to $0.30 in 2020.

Why is this important? Sensors have been termed the “nervous system” of IIoT. At the most basic level, IIoT software requires real-time access to factory asset sensor data to provide intelligent recommendations that relate to production, asset maintenance and supply chain management.

The Application of Machine Learning to Asset Maintenance
Today, the default Predictive Maintenance (PdM) systems use SCADA data to monitor asset performance. Manual thresholds are set based on human-made rules and when sensor data breach thresholds an alert is triggered signaling potential machine fault. Based on bandwidth constraints, a small number of factory sensors are monitored this way.

With Machine Learning, an algorithm is trained to detect abnormal and correlated patterns of abnormal sensor data. Based on this behavioral analysis, the algorithm identifies machine degradation or fault before they occur. Machine Learning does not require rules or simplistic threshold setting, because it is looking at behavioral patterns. Vast amounts of data can be analyzed in real time without the need for human involvement.

What is Automated Machine Learning?
Only a few years ago, research in the field of Artificial Intelligence was conducted at an academic level. In the last couple of years, there has been significant investments in Machine Learning R&D. Both industry juggernauts such as Siemens and GE and VC-funded startups are accelerating the rate of innovation.

Today, a plethora of Machine Learning and Deep Learning algorithms can be applied to Smart Factory applications. Given vast amounts of data that are relatively inexpensive to analyze, the key today is to automatically select the optimal Machine Learning algorithm. There are dozens of Machine Learning and Deep Learning algorithms and preprocessing and data cleansing methods. Furthermore, the hyperparameters of the selected method need to be tuned and the number of trees (or the number of layers and nodes at each layer) need to be calibrated.

What does this mean for the factory owner? How well Machine Learning and Deep Learning is applied has a significant impact on the performance of the model. With Automated Machine Learning, the algorithm is trained to select the right model to use, how to use it without the input of a data scientist and without delay.

What’s Preventing Widespread Adoption?
In our estimate, less than 5% of sensor data is ever analyzed by PdM tools. Given the reduction of cost in computing and sensor costs, cloud-based Machine Learning should easily dominate the PdM category. However, one of the most significant constraints is tactical: facility owners often lack access to the sensor data that their own machines generated. In some cases, third-party vendors are the gatekeepers of internal data and lack the incentive to help factories migrate to Machine Learning based Predictive Maintenance.

Summary and Conclusion

Many of the enablers for Machine Learning are in place: inexpensive, cloud-based computational capabilities and advanced Machine Learning algorithms that can be applied to sensor data. In the short and medium term, facility owners will need to define their Machine Learning strategy and then find ways to overcome internal obstacles that are preventing data access and analysis.

As more industrial plants use sensor-generated data to predict evolving machine asset failure, it will create momentum in the Machine Learning Predictive Maintenance category. Cloud-based solutions are now ubiquitous in the corporate IT environment and it is inevitable that this technology is applied to Big Data.

It is not a question of whether Machine Learning for Predictive Maintenance will be applied. Deployment is a function of the maturity of each industrial plant and their technology receptibility and roadmap.