Predictive Maintenance: Harnessing AI for unmatched machine uptime

Maintenance strategies have remained relatively static over the last century – until an anomaly causing aircraft downtime changed everything for the better. See how predictive maintenance has evolved with AI, and why it is a major breakthrough in the Industry 4.0 paradigm.


Industrial production systems have progressed towards more and more advanced versions over the last two centuries. While Industry 3.0, which marked the shift from analog to digital control systems unleashed new possibilities, few had anticipated that there was room for further advancement. 

But all of this changed over the last decade, when developments in network and sensor technology, and artificial intelligence and machine learning unveiled the possibility of yet another industrial revolution – a paradigm which was named Industry 4.0. It was envisioned that factories would run themselves, production systems would optimize themselves, and peak efficiency would become the baseline of operations.

While we may still be a decade away from unmanned production floors, Industry 4.0 visions are now materializing fast – especially in the context of maturing technologies like predictive maintenance. In this article, see how predictive maintenance redefined traditional maintenance workflows, how it works, and where it’s headed.

A brief history of machine maintenance

In production plants of the 1950s, heavy machinery was typically maintained in a reactive and proactive fashion. Experienced plant operators would intuitively determine maintenance schedules based on their years of experience, and if something went wrong, they would dispatch a team of technicians to diagnose and fix the issue.

Even when SCADA systems were introduced in the 1970s, they did little to change maintenance strategies. Sure, it was now possible to monitor systems, but technicians could not see an oncoming fault – much less determine a course of action to prevent it.

Things stayed relatively the same for 2-3 decades, until CH Waddington, along with two Nobel laureates, looked into the problem of aircraft availability where repeated inspections were followed by breakdowns. This was termed the Waddington effect, and it paved the way for the development of the very first predictive maintenance systems. The objective, in line with Waddignton’s advice, was to tune the maintenance process to the actual condition of the equipment.

Under the hood of a predictive maintenance system

Today’s predictive maintenance systems have evolved from sensor technology which was employed to build remote control systems for plant machinery, and advancements in AI and ML inference. 

A typically predictive maintenance system makes use of two key components – first of which, are the IoT sensors which are installed on machine systems. These are typically vibration and temperature sensors, but some systems also use acoustic, pressure, voltage, and humidity sensors, gyroscopes, and even accelerometers. However, temperature sensors and vibration sensors are the most widely used ones, found in ~61% and 46% systems respectively..

Sensors are then integrated into a network with wired or wireless technology, which enables them to emit data to computer systems. When sensor data is recorded in digital systems for a long time, it contains the readings associated with healthy and faulty states, and everything in between.

This data becomes the foundation for the second key component of a predictive maintenance system – i.e., AI and ML algorithms. These algorithms are trained to learn the various trends that this data has to offer. For example, an algorithm may be trained to read spikes in temperatures as anomalies – and the machine state associated with temperature spikes may be labeled as a faulty state. 

Now, when this algorithm is run on data that is being emitted by sensors on a machine system in real time, it will immediately alert engineers of small spikes in temperature – and warn them of an oncoming failure. In reality, it is possible to detect an upcoming failure weeks, or even months in advance

A predictive maintenance solution could make use of various algorithms – like neural networks, XGBoost, Regression techniques, or nearest neighbors algorithms. The selection of algorithms depends on the performance of each algorithm for a given task. For instance, a neural network may be better at catching all turbine failures, but an XGBoost technique may be able to offer a bigger window between the alert and the actual failure.

Moving beyond artificial intelligence in predictive maintenance

While AI-powered predictive maintenance systems use historical data to predict outages, their accuracy may vary over time due to changing plant conditions. While these algorithms are able to account for structured variations in time cycles (like annual, monthly, or weekly patterns), their accuracy may falter with equipment from different manufacturers, change of input materials, or irregular weather trends, in some scenarios.

To account for such outlier cases, some predictive maintenance systems make use of self-learning systems, which constantly evolve the behavior of AI models to maintain precision. They may use reinforcement techniques to reward accurate predictions, or require data science experts to update the models. One benefit of sourcing predictive maintenance technology from innovative players in the market is that businesses can exploit such developments without having to maintain investments in data science talent.

What’s next for predictive maintenance?

Predictive maintenance systems have now evolved, not only to predict an outage associated with an equipment, but also the likely cause of that upcoming outage. These systems are trained to predict the particular failure modes (set of all causes that lead to machine failure), and enable technicians to carry out targeted maintenance procedures accordingly.

Predictive maintenance is now an industry-wide phenomenon in oil and gas, manufacturing, cement, and power and utilities sectors. Because it is no longer a differentiating capability, leading companies lean towards souring the technology from innovative companies in the market – unless they are looking to build a custom use case. 

Such providers make it possible for mid-sized businesses to leverage cutting-edge systems without having to invest in data science, cloud, and software development talent. This unlocks a new ceiling of operational efficiency, characterized by high machine availability and fully-optimized maintenance costs. Time sensitive predictive maintenance use-cases also make use of edge servers to make inferences and alert technicians – thereby enabling responsive and centralized, yet agile plant operations.

Final words

The use of AI in predictive maintenance has fundamentally altered the meaning of optimal with respect to machine maintenance and production efficiency. Higher availability and improved workforce efficiency fueled by predictive maintenance can boost profitability levels by up to 10% for some organizations. 

In other words, predictive maintenance is a key technology for recovering production losses caused by outages – which amount to $750bn in the US alone. However, investing in the right solution is crucial for achieving these gains – and this requires businesses to understand the importance of AI techniques that drive a predictive maintenance platform.

Unleash the gains of predictive maintenance with Uptime AI, which is powered by the most sophisticated machine learning techniques in the industry. Contact now.

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