Predictive maintenance is a game changer

The cost savings offered by artificial intelligence-powered predictive maintenance, compared to standard preventative maintenance, is a game-changer for mining and other commercial enterprises.

"Reactive maintenance costs enterprises billions each year in lost production. The ultimate goal is not to replace machines or parts too early but service them at the right time," said Vankata Naveen, disruptive tech analyst at GlobalData. "This can be done optimally using AI-powered predictive maintenance leading to reduced downtime, extended equipment life, improved safety and increased return on investment."

With its Disruptor database, GlobaData has the capacity to analyze emerging tech-enabled opportunities with must-have information on promising start-ups, technology led innovations, latest sector trends, consumer insights and venture capital portfolio investments.

Innovation Explorer, a database within the analytics and consulting company's larger Disruptor database, reveals how predictive maintenance is increasingly becoming useful to foresee machine failures well in advance and save costs to enterprises across industries such as aerospace, automotive, mining, manufacturing, oil and gas, and power.

Currently, several companies under this umbrella have begun to see effective results.

In the manufacturing sector, Mitsubishi Electric has developed an AI-based diagnostic technology that harnesses machine learning algorithms to analyze sensor data of machines to generate a model of the machine's transition between different operational states.

Even an insurance company has begun to offer a smart home insurance product for U.S. consumers, American Family Insurance in collaboration with UK insurtech startup Neos. By leveraging AI algorithms to analyze an individual's water usage over a period of time, a pattern is established, with any changes to the pattern it is capable of predicting potential problems with pipes.

Of course, in one of the largest industries in the world, the mining sector, Canada-based gold miner Agnico Eagle Mines has partnered with Montreal's Newtrax Technologies to predict mobile equipment maintenance issues in advance using AI and paired internet of things sensors.

Even still, instead of building an AI-powered predictive maintenance system from scratch, enterprises are partnering with startups to deploy their solutions off-the-shelf.

These include companies such as C3.ai, Uptake Technologies, Maana, Sight Machines, Predictive-Sigma and Presenso.

"One of the critical challenges with predictive maintenance is to streamline the flow of data from machines to a central system with low levels of latency and high security. However, these issues can be addressed with upcoming technologies such as 5G and advanced security," said Naveen. "Despite stumbling blocks, AI-based predictive maintenance techniques are materially crucial for an enterprise to effectively predict operational breakdowns and thereby save costs."

Find out more about two tech start-ups – RIO Analytics and OneWatt – that have developed AI-powered predictive maintenance solutions for mining at A predictive shift for mine maintenance in the June 3 edition of Metal Tech News.