Intelligent predictive maintenance ensures operational reliability

Published 14 August 2023. Updated 25 May 2026.

Intelligent maintenance based on measurement and data analysis promotes sustainability, improves competitiveness and serves as a strategic tool. Intelligent maintenance reduces the risk of production failure because it is guided not by operating hours or production volumes, but by condition changes predicted from regular measurements. Predictive maintenance, in turn, refers to need‑based, planned maintenance actions carried out in advance to optimize production capability and the lifecycle of production equipment. This ensures operational reliability and extends service life.

An intelligent maintenance system, among other things, supports defining the content of planned maintenance shutdowns and ensures the availability of critical spare parts by ordering them in advance.

Maintenance calculation models create the intelligence

In data‑driven intelligent maintenance, the “intelligence” is formed by different calculation models, which can be executed in a cloud environment or as edge computing closer to the production equipment. The choice between these depends on requirements for real‑time performance, the amount of data needed and computing power.

Maintenance calculation models vary in scope. At their smallest, a model may cover condition information for a single component of a device, such as a shaft bearing—or even a bearing sub‑component, such as the inner ring or cage. At their largest, a model can cover an entire production facility with hierarchical models and estimates of replacement needs for different parts during the next planned maintenance shutdown.

Optimized predictive maintenance based on effective maintenance models is reflected not only in financial metrics but also at every level of OEE (Overall Equipment Effectiveness), which measures production efficiency (availability, performance and quality).

Data reveals maintenance needs

Data used in maintenance analytics, obtained continuously from a production process or discrete manufacturing, can indicate maintenance needs indirectly or directly.

A decline in production efficiency may suggest equipment wear and an emerging need for maintenance.

Vibration data from an accelerometer attached to a device—and new frequency peaks at specific frequencies related to rotational speed and bearing dimensions—can point almost directly to a crack in the bearing’s inner ring.

Harder‑to‑detect phenomena include material fatigue and abnormal operating modes or failures that are more difficult to predict, especially when there are no prior examples of them in the data.

Ready‑made calculation models to support maintenance

Manufacturers of devices or components also provide maintenance teams with various ready‑made calculation models and formulas. In the simplest models, there is only one parameter. An example is RUL (Remaining Useful Life), which may be based solely on operating hours. However, an overly simple model is only indicative and does not account for environmental impact in any way.

More complex models include more variables to improve accuracy. However, these often lack time feedback and cannot utilize time‑series data that would better represent varying use and conditions.

If the operating environment and usage differ from the standard, it can be difficult or impossible for the manufacturer to provide a reliable model for maintenance use. If none of the available models are reliable, the user must build their own intelligent maintenance model based on the data available.

The foundation is relevant data and an understanding of production and equipment

Data collection benefits greatly when the data platform supports the use of data integrations, defining new data to be collected, and retrieving historical data. Although today’s Big Data era enables the collection of vast amounts of data, the most important thing is to focus on collecting the right, necessary data.

Typically, phenomena relevant to maintenance develop slowly, and intelligent maintenance models collect data over long periods. If the phenomenon indicating a maintenance need occurs rapidly, it usually means that a new sensor is needed to provide better data—so that there is time to take maintenance actions before the device or component fails.

Once the required data is available, the next challenge is analyzing it and building a model that supports maintenance. The model can be a simple linear model, a statistical model, or at its most complex a multi‑layer neural network capable of processing time‑series data.

Correlation between collected data and maintenance events alone is not enough to build an effective maintenance model; data analysis must be combined with an understanding of production and equipment. For example, motor load data collected from an automation system loses meaning if it is not known which specific motor unit has been running and which has been under maintenance. Manually entered data should be treated with great caution and verified, for example, through an ERP system. The system must surface only the most relevant findings from a large analyzed asset base for the maintenance manager to investigate. If a finding is urgent, it should be immediately communicated to production as well.

Insta’s intelligent cloud‑based data platform for industry

The costs of energy and other utilities have risen, and companies are therefore looking for ways to improve productivity. As technology develops, the possibilities for intelligent maintenance continue to evolve.

Data‑driven solutions make it possible to get more out of the existing installed base and improve the predictability of maintenance. With Insta’s Industrial Data Platform solution, the customer receives everything from data collection to a ready‑made solution that guides operations. Getting started is affordable, low‑risk and fast. No major investment such as a new production line is needed—instead, data helps make the most of the tools a company already has.Large enterprises have already built their own cloud platforms because they have been able to invest in data utilization. Now it is the smaller companies’ turn.

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