When managing the maintenance practices for a facility it's important to understand the different approaches and the benefits of each. At the core, maintenance styles can be classified into a few different categories. Some of the most common approaches include reactive maintenance and preventative maintenance. You may already be familiar with those or even practicing them yourself, but there are more efficient styles quickly gaining traction. Predictive maintenance (PdM) is defined as maintenance practices designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed rather than on a preset schedule (preventative).
Mike Brooks proposes the use of machine learning software to improve plant reliability and to reduce unplanned downtime. There is a significant need to carry out failure prevention using data-driven truths instead of guesstimates, evidenced by the fact that a combination of mechanical and process induced breakdowns account for up to 10% of the worldwide $1.4 trillion manufacturing market, according to a 2012 report from The McKinsey Global Institute. While companies have spent millions trying to address this issue and ultimately avoid unplanned downtime, only recently have they been able to address wear and age-based failures. Current techniques are not able to detect problems early enough and lack insight into the reasons behind the seemingly random failures that cause over 80% of unplanned downtime. This is where using machine learning software to cast a'wider net' around machines can capture process induced failures.
The emerging role of Data Science, commonly known as "Big Data" in railroad maintenance, was discussed earlier in Railway Age ("Better Railroading Through Big Data.") As noted, railroads are developing and implementing new generations of sophisticated inspection and monitoring systems, and as a result are finding themselves collecting large volumes of data. This large volume of data, often referred to as Big Data, generally refers to data sets that are so voluminous and complex that traditional data-processing application software is inadequate to deal with them, resulting in the need to use advanced data analytic tools. This is illustrated in Figure 1, which compares the traditional data analysis approach to data handling with the Big Data approach. This use of Big Data in the railroad industry, to include freight and passenger rail, cuts across traditional departmental lines, with applications in Engineering (Track and Structures), Equipment (Rolling Stock) and Transportation (Operations).
In 2017, McKinsey conducted a study on productivity gains driven by technology transformations, such as the steam engine, early robotic technology and advances in information technology. McKinsey sees manufacturing on the brink of the next generation of industrial automation revolution with unprecedented annual productivity growth of between 0.8 – 1.4% in the decades ahead. Advances in robotics, artificial intelligence and machine learning will match or outperform humans in a range of work activities involving fast, precise, repetitive action and cognitive capabilities. To remain competitive, complex industries need to deploy industrial automation more than ever, as intense global competition drives process industries to increase efficiency through reduced operating costs, increased production, higher quality and lower inventories. The highest priority should be to eliminate production losses caused by unplanned downtime and address a $20 billion a year problem for the process industries.
If you just include "machine learning" in your pitch you can add a zero on to the end of your valuation. Machine Learning (ML) and the Internet of Things (IoT) are huge buzzwords right now, and they're both near the peak of the hype cycle. The above quote came somewhat jokingly from an investor, but it has some truth to it too. Given all the hype and buzz around machine learning and IoT, it can be difficult to cut through the noise and understand where the actual value lies. In this week's #askIoT post, I'll explain how machine learning can be valuable for IoT, when it's appropriate to use, and some machine learning applications and use cases currently out in the world today.