One subset of the internet of things \u2013 the industrial IoT \u2013 adds new capabilities to operational technology including remote management and operational analytics, but the number-one value-add so far has been predictive maintenance.\nCombining machine learning and artificial intelligence (AI) with the deep pool of data generated by the flood of newly connected devices offers the opportunity to more deeply understand the way complex systems work and interact with each other.\nRELATED:\u00a0Tips for security IoT on your network |\u00a0Most powerful IoT companies\nAnd that can promote predictive maintenance - with the ability to pinoint when components of industrial equipment are likely to fail so they can be replaced or repaired before they do, thereby avoiding more costly damage and downtime.\nFine tuning IIoT predictive maintenance models\nAccording to Wael Elrifai, senior director of sales engineering and data science at Hitachi Vantara \u2013 the company\u2019s IoT arm \u2013 one of the complexities of predictive maintenance is that AI-produced models for system behavior have to change over time. He used the example of a Hitachi Vantara railway customer with a 27\u00bd-year maintenance contract to illustrate the issue.\nAs train parts age, they respond to stresses differently than they do when they\u2019re new.\u00a0 Because of that, maintenance schedules should be adjusted over time to take into consideration changing failure rates. These schedules can be generated with models that are the output of machine learning, he says.\nThere's a "bathtub curve" to equipment failure, Elrifai said. At the beginning of its service life, there are frequent failures, but maintenance processes get figured out as time passes, so failures become much rarer. "And then, of course, end-of-life \u2013 it starts to fail a lot again," said Elrifai.\nThis type of AI-produced model can be created for other industries as well, and Hitachi has just released a platform called Lumeda that pulls in IIoT data that data scientists can use to adjust their machine-learning models more precisely. \u201cIt\u2019s all about being able to monitor machine-learning-model accuracy after a model goes into production,\u201d said Arik Pelkey, senior director of product marketing.\nOne example is a chemical-manufacturing process. Lumada creates a centralized data pool on which data scientists can experiment, so the process of testing different models against each other means that the company can change its inputs and get a more accurate prediction of what's going to happen to the chemicals at the other end of the production line.\nElrifai and Pelkey said that the biggest impact that evolving machine-learning-model management will have will be on low-margin, high-capital businesses, like heavy industry and transportation.\nIIoT preventive maintenance in cars\nCars manufactured in the past 15 years generally have a computer on board called OBD-II, which stands for on-board diagnostic, version 2. If you\u2019ve seen a mechanic plug a scanner into a specialized port on your car, they\u2019re probably checking with the OBD-II.\nA startup called TheCarForce is looking to leverage the data from that computer to help drivers and garages \u2013 and ultimately, even manufacturers \u2013 alike. CarForce\u2019s hardware is a dongle that plugs into that port and stays there, sending diagnostic data, via a SIM card, back to a central hub.\nCarForce founder Jessika Lora said that modern cars are collecting more self-diagnosis data than the space shuttle. But once gathered, the data doesn\u2019t get stored and used for analytics. \u201cIt went to the car\u2019s computer, and it got discarded immediately," she said.\nCarForce is mostly focused on selling its product to garages, but Lora said that the potential beneficiaries are numerous. In the garage use case, mechanics can get real-time maintenance data from vehicles they service, which offers both the ability to warn customers of impending problems and to correlate large data sets together to help predict future reliability issues.\nIt's a value-add because the garage can stay a step ahead of mechanical issues \u2013\u00a0 an alert goes off, and the garage can contact the customer to schedule maintenance. Even an awareness that customer X might be coming in for an oil change on a given day can help with planning and scheduling.\n\u201cIf you look at the big data\/AI path, step one is just seeing the data,\u201d said Lora. It\u2019s part of what she refers to as the \u201clilypad\u201d approach to development \u2013 building one system to enable a leap to the next lilypad, and so on.\nCarForce plans to operate on a population level - predicting reliability and failures across big swaths of the automotive landscape.\n\u201cSo we can actually start making recommendations not just to garages, but to the manufacturer of the car as well,\u201d Lora said. \u201cWhen we see these three faults occur in tandem, it means that thing X is about to happen to your car.\u201d\nIoT predictive maintenance and farm equipment\nTravis Senter of Senter Farms, works about 20,000 acres of row crops in northeast Arkansas, about 40 miles north of Memphis, in the Mississippi River delta. Cotton, long-grain rice, soybeans, corn and wheat. 23 tractors, three combines, two cotton-pickers and four sprayers all hooked into John Deere\u2019s JDLink agricultural IoT system.\n\u201cWe need this technology to be able to track and see where things are. And if there\u2019s something going on, we need to make sure we can fix it in a hurry, because you can\u2019t afford downtime,\u201d said Senter. \u201cYou\u2019ve got your back against the wall every day with weather, with timing, with planting.\u201d\nThe busy season lasts from roughly mid-March through late October, and machines have to be fully available throughout that time. Deere analyzes even minor alerts \u2013 what Senter says might be considered \u201cnuisance\u201d alerts to the operator on the ground \u2013 and uses them to draw patterns and conclusions about reliability and service data.\nDeere\u2019s IIoT team performs high-level analysis on the data it gets from connected machines and has helped Senter\u2019s operations materially.\n\u201cFor example, we had a fan drive on the front of an engine, and it would cause a small vibration. The system would detect it, send an error code, you\u2019d look at it, it looks fine,\u201d he said. \u201cWell, [Deere] looked at this stuff, and they\u2019re constantly getting requests to shut some of these off, because they seem like nuisance codes.\u201d\nThat vibration, however, turned out to be sign of incipient failure on 10 of the 13 tractors the code showed up on. \u201cThey were able to fix it with maybe a $200-$300 fix, instead of a $6,000 fix to replace that entire drive,\u201d Senter said.