Many Polish manufacturing companies view the concept of predictive maintenance and artificial intelligence with trepidation. The prospect of fully digitizing their machinery, the costs, the need to integrate legacy systems, and the specter of complex algorithms often lead to project halts before they even begin. However, the key to success lies in a shift in perspective. Implementing AI isn't a technological sprint for the entire organization, but a precise strategic leap. It's a mistake to believe that the first step is a mass purchase of sensors and expensive software. Instead, every maintenance specialist should ask themselves which component's failure would be most painful for the company. Starting this process correctly, by accurately pinpointing a single critical machine , is the only way to achieve a rapid return on investment and build internal confidence in the new technology. In this article, we'll outline the essential steps for a methodical pilot, proving that AI is within reach for anyone who knows where to begin.
Step #1 - preliminary analysis and definition of the pilot goal
The process of implementing AI in maintenance must begin with a business decision that will determine the return on investment. This is the most crucial step, and it involves deliberately limiting ambitions. Monitoring the entire factory at once is a mistake. Instead, apply the 80/20 rule and select a single, most critical machine or component , such as a key motor or CNC machine spindle. This should be the component whose failure generates the highest downtime costs per hour, is difficult to predict using traditional methods, and additionally requires specialized electronics repair, the delivery of which extends downtime. Only such precise targeting will ensure rapid and measurable success.
Before a pilot project can begin, it must have a specific business goal . It's not enough to simply say you want a machine to perform better. The goal must be measurable. For example, set a goal of reducing unplanned downtime by 30 percent on a selected machine within six months, or reducing repair service costs by 25 percent. Such a specific, achievable goal will convince decision-makers to continue the project and provide initial ROI data.
Equally crucial is the appointment of a project leader. It's essential that one person from the maintenance or IT department becomes the project manager. This person must not only understand the problem but also have decisiveness or direct support from decision-makers. Without a strong leader, a project, even with the best algorithm, will stall at the systems integration stage or due to internal resistance.
#2 step - data audit and sensor selection
Once you've chosen your critical machine, you need to address the key currency in Predictive Maintenance: data . Artificial intelligence is only as good as the data it's trained on.
This step begins with an audit of available historical data. It's important to examine what data already exists in SCADA systems, PLCs, CMMS systems, and even in the Excel spreadsheets maintained by technicians. While this historical data may not be perfect, it provides a starting point for training the first simple models. However, it's important to remember that poor data quality, resulting from manual entry errors or inconsistencies, is one of the biggest challenges. Processes must be established in advance to ensure regular data review and cleansing.
Next, we move on to establishing the first sensor layer. Sensor installation begins at a selected critical element and there's no point in massively scaling it. These are most often vibration sensors, which provide information for mechanical diagnostics, and current and temperature sensors, crucial for electrical diagnostics. This data must be transferred to the IoT stream, and from there to a time series repository for analysis. It's crucial at this stage to ensure a unified transmission channel, for example, using the OPC UA or MQTT standards.
#3 step - piloting and building the first model
Once the data is flowing, there's absolutely no point in waiting for a perfect set of information. Testing should begin as quickly as possible. Instead of implementing complex and expensive neural networks, the approach is to start with simple machine learning models. These algorithms can successfully identify anomalies—any deviations from the norm—based on historical machine behavior. This approach minimizes risk and delivers rapid results.
It's crucial to set alarm thresholds adaptively. The AI system must dynamically set these thresholds, taking into account the machine's operating history and context, rather than rigidly, as is the case with traditional sensors. This approach reduces false alarms, which would quickly undermine technicians' confidence in the new technology.
Human verification and optimization are essential at this stage . It's important to remember the technician. Every result and every anomaly generated by the AI must be verified by an experienced maintenance employee. It's the human who teaches the algorithm which of its warnings are actually significant and costly to the company, and which can be ignored. After the first month of operation, the effects are measured, followed by iteration, i.e., model optimization. This may mean adding new process parameters to the analysis or adjusting the sensitivity threshold. This is a cycle of continuous learning, in which the technician serves as the trainer for the AI.
Summary of key activities
The lessons learned from the methodical implementation of artificial intelligence are clear. Success in predictive maintenance depends not on the complexity of algorithms, but on strategic discipline . It's business choices, not technical ones, that determine project success. The right path to AI requires maintenance managers to overcome the temptation to scale and focus on a single, measurable problem . Defining the most costly failure and building a small pilot around it is a sure path to achieving a rapid return on investment and gaining internal confidence in the new technology. This methodical, phased approach, supported by technicians' experience in data verification, ensures that the digital transformation will be controlled, and its effects will be visible after the first few months of operation. By starting small, facilities have the opportunity to become leaders, not just followers, in the era of Industry 4.0.
