Machinery Lubrication magazine published by Noria Corporation
Issue link: https://www.e-digitaleditions.com/i/1433576
www.machinerylubrication.com | November - December 2021 | 11 ML ML ML A lmost every day, impressive new developments in machine learning and artificial intelligence (AI) are reported, such as: • A computer program that taught itself the strategy game "Go" and beat the current world champion • Impressive advancements in facial recognition software • Almost instantaneous translation of one language to another (either written or spoken) using freely avail- able online software With all these advancements, it is natural to ask whether such techniques could be effectively applied to lubrication problems. The rise in these many examples of machine learning/artificial intelligence is directly linked to the availability of high- quality data (and lots of it). Machine learning and artificial intelligence can generally be split into two types: supervised models and unsupervised models. Simply put, super- vised models typically find statistical (or regression) models relating input data to output data and provided new input data that is covered by the training data set. ese techniques should work well, although they are not able to reliably extrapolate if the new input data is outside that of the training set. Unsupervised models typically use neural networks. Neural networks, also referred to as "deep learning", are a collection of algo- rithms loosely modeled on the human brain, designed to recognize patterns. ese unsu- pervised models can effectively "work out" the best model for themselves — however, once a good model has been found, it is not always obvious why it works. In other words, the underlying algorithm that the machine learning model has found is not easy to unravel. ere are numerous examples where artificial intelligence and machine learning have already been applied within the lubrica- tion/tribology community, including: Condition monitoring — Machines are now being supplied with an increasing number of sensors that can report their operating conditions (speeds, loads, lubri- cant temperatures) back to the OEM and/ or machine owner. ese machines can be fitted with additional sensors that monitor vibrations and electrical currents going in and out of the machine. Lubricant sensors can also detect wear particles or monitor lubricant degradation (such as dielec- tric-based sensors). By monitoring a large number of machines, warning signs of likely future failure can be predicted based on previous failures and correlating such failures with the various sensors data. Customers can be advised to service their machines or replace specific components once early signs of failure are detected. Even better, they may catch the root causes before they lead to failure symptoms. e schematic in Figure 1 below shows the type of signals that can be picked up before failure occurs. e most commonly used techniques for condition monitoring are vibration monitoring and thermography (looking for "hotspots"). A number of commercial solutions are available today that use arti- ficial intelligence and machine learning for condition monitoring (from companies such as SKF, GE, Siemens, and Bosch). ere are also numerous start-up companies using more specialized techniques, such as ultra- sound (UK-based Tribosonics; US-based UE Systems, with their OnTrak SmartLube system). For high-value machines, other specialized techniques such as lubricant monitoring by infrared and wear particle sensors may also be used. Although the general application is to advise customers of machines that may need service or components replaced, these techniques can also be used to monitor energy consump- tion, and customers can be given advice on how to reduce their energy consumption (and related cost savings can help pay for such monitoring systems). Chatbots for Lubricant Helplines — Shell offers an online chatbot (Lubechat) that can answer simple customer queries and provide technical data on lubricant products. e 24/7 service is available in twelve countries and several different languages (including Chinese, Russian, German, etc.) Questions that cannot be answered by the Virtual Assistant are passed on to human experts for answers. Fleet Telematics — ere are commer- cial companies (such as Microlise) that extract data from heavy-duty vehicle elec- tronic control units (remotely). e data contains speed, load, temperature data, route data and fuel consumption data. By combining data from many trucks, the most efficient routes between two or more places can be identified. is data can also identify improvements in logistics efficiency and fuel-heavy drivers (who can then be given training into how to reduce their fuel consumption). ML Figure 1: The types of events preceding machine failure.