Predictive Maintenance in machinery and equipment: Why the Data Foundation Decides on Success

Machinery and equipment manufacturers equip their most critical systems with sensors, purchase an AI platform, and launch a predictive maintenance project. After a few months, the results are sobering: Downtime has barely decreased, and the predictions are too unreliable to base service calls on. The first suspicion falls on the model, which is thought to be too inaccurate. However, the problem rarely lies with the model itself, but rather with the data it draws upon. Predictive maintenance promises fewer unplanned downtimes, predictable service calls, and a longer service life for machines. The bottleneck is rarely the math. It lies in the data foundation: service data is scattered across ERP systems, Excel spreadsheets, service reports, and the minds of experienced technicians—often without a clear link to the individual machine in the field. For those looking to implement predictive maintenance, they decide on success first and foremost based on the data foundation, not on the choice of algorithm. First, here’s what matters:

• The bottleneck is the data foundation. Without structured, machine-related data, any forecast remains unreliable, no matter how good the model is.
• Three types of data must be collected for each machine: usage data, live sensor readings, and service history.
• The Digital Machine File consolidates this information into a single, reliable data source —only then can diagnoses, forecasts, and service decisions be made with confidence.
• A pragmatic approach: assess your own data (Installed Base Assessment), then start with a pilot machine.

What Machine Data Predictive Maintenance Really Needs

Three types of data are essential for reliable predictions: usage data, live sensor data, and service history. Only when these data points are analyzed together can we determine when a component is likely to fail. What decides here is not the volume of data, but whether the data allows conclusions to be drawn about the condition and wear of a specific machine.

Operating Hours, Cycles, and Usage Data

Operating hours and cycles form the basis for assessing a machine’s condition. This data often comes from the control system or from IoT sensors and reflects not only the total operating time but also the intensity of use. This allows maintenance to be planned based on actual needs rather than rigid intervals, and makes it easier to estimate the remaining service life of heavily used components.

Sensor measurements: vibration, temperature, pressure

Live sensor data detects wear early on. Abnormal vibration patterns indicate bearing damage; a rise in temperature points to overload or a problem in the cooling system; and pressure fluctuations indicate leaks. These values primarily indicate acute problems. They only reveal their full value when viewed in the context of a machine’s history.

Service History: Repairs and Maintenance Records

Comprehensive documentation of past maintenance operations reveals patterns in machine behavior. Which parts were replaced and when, which malfunctions occurred repeatedly, and how long each repair lasted. This history puts current sensor readings into context: a vibration reading takes on a different meaning if the same bearing was already replaced eight months ago. If the history is scattered across operation reports and emails, this context is lost.

Why Predictive Maintenance Projects Fail Due to Data Limitations

Many manufacturers have data that could be used for predictive maintenance. However, this data is scattered, incomplete, or locked away in legacy systems. Maintenance logs are stored in Excel, sensor data is in the control system, and service reports are in email inboxes. Without consolidating this data, even a good model cannot identify useful patterns. Industry analyses reach a similar conclusion: Data quality, data availability, and organizational hurdles hinder predictive maintenance initiatives more than the accuracy of the algorithms. And even good data remains ineffective if processes aren’t adapted and insights sit unused in a database. On top of that, there’s a context issue. A model may flag a value as out of range, but it cannot specify which machine is involved, its year of manufacture, its current configuration, which customer owns it, or what repairs were last performed on it. It is precisely this context that decides whether an anomaly leads to a service decision.

Structuring the Data Foundation: The Digital Machine File

The starting point is a structured installed base. The Digital Machine File consolidates all information about a piece of equipment in one place, making it a reliable source of data for forecasts. This includes:

• Master Data: Serial Number, Year of Manufacture, Configuration, Installation Location, Contract.
• Maintenance History: Repair reports, replaced parts, service intervals.
• Real-time sensor data: vibration, temperature, pressure, operating hours.

Only when static master data and dynamic sensor data are combined can a sensor alarm be properly classified and a wear pattern detected early. Older systems without a digital interface can be retrofitted for this purpose: Sensors measure vibration, temperature, or pressure, while IoT gateways convert proprietary control formats into a standardized structure. This data is automatically assigned to the respective system, so that service requests are triggered automatically when critical thresholds are reached, without the need for manual intervention. The article “Making IoT Data Usable in the Service Portal” demonstrates how sensor data can be utilized in the service portal. The quickest way to determine the current state of your data is to conduct an Installed Base Assessment. It verifies the completeness and quality of existing data, identifies gaps, and helps prioritize critical systems whose failure would be particularly costly.

The Four-Step Path to Predictive Maintenance

Predictive maintenance develops gradually, not through a single technological leap. Investing directly in complex predictive models without laying the groundwork will waste time and money. A sustainable approach follows four stages based on the maturity of the data infrastructure.

• 1. Digitize: Organize data and the installed base. Build an equipment catalog, identify data sources, verify data quality, and consolidate everything into the Digital Machine File. The starting point is a maintenance audit: Which machines break down most frequently, and which downtime costs the most?
• 2. Connecting: Connecting processes and enabling diagnosis. Real-time sensor data is integrated into maintenance processes. Condition-based maintenance continuously monitors equipment and automatically triggers service requests when defined thresholds are reached.
• 3. Decide: Determine the right time for maintenance. As the data history grows, forecasts become reliable enough to ensure that action is taken neither too early nor too late. This is where Service Decision Intelligence comes in: an intelligence layer that uses sensor readings, machine context, and service history to generate a well-founded recommendation, rather than simply issuing an alert.
• 4. Automate: Moving Toward Autonomous Services. Forecasts are turned into recommendations for action and scheduled maintenance orders. The system suggests the most appropriate course of action before a failure occurs and continues to learn from each intervention.

The difference between Level 2 and Level 3 is the real leap forward. A “Vibration Increased” alert is not yet a decision. Only once it is clear which bearing on which machine is affected, which technician would be available and when, and which spare part is needed, can the service team plan rather than react.

Check Data Quality and Start Small

Before investing in sensors and software, you need to take stock of the current situation. Which machines break down most frequently? Are the maintenance records complete? Are vibration, temperature, or pressure even being measured? Typical weak points include poorly calibrated sensors, gaps in data transmission, and inconsistent data formats with different units. After that, it’s advisable to start small: a critical machine whose failure is particularly costly, equipped with condition monitoring and automatic alarms. Every maintenance task performed is logged in the system. This data forms the basis on which a model can learn actual failure patterns in the first place. Once a successful pilot has been completed, the approach can be rolled out to other systems. This article compares whether the predictive approach is more cost-effective than preventive maintenance Predictive Maintenance: A Comparison of Costs and Benefits.

Conclusion: Data quality comes before predictive maintenance

Predictive maintenance is not a product you simply turn on; rather, it is a development process based on a robust data foundation. Without structured maintenance histories, complete master data, and reliable sensor readings, any prediction remains a guessing game, no matter how good the model is. The first step, therefore, is a practical one: assess your own data. An Installed Base Assessment identifies gaps and sets priorities, while a Digital Machine File provides the structured foundation for all subsequent steps. If you’d like to find out where your equipment stands today and which machine is a good place to start, schedule an introductory call.