Predictive maintenance is a maintenance strategy based on operational, condition and historical measurement data. Its purpose is to detect emerging deviations, wear or an increased probability of failure at an early stage and to align maintenance activities with the actual or predicted condition of an asset.
Predictive Maintenance at a Glance
- Predictive maintenance evaluates current and historical operational data to identify abnormal developments before a failure occurs.
- Rules, statistical methods or machine-learning models compare asset behaviour with reference values, similar components or expected operating conditions.
- The results provide indications for inspections and maintenance activities but do not automatically replace a technical fault diagnosis.
- Reliable predictions require dependable measurement data, sufficient data histories and suitable reference models.
How Does Predictive Maintenance Work?
Predictive maintenance is based on the continuous or periodic collection of relevant operational data. Depending on the asset, this can include power, voltage, current, temperature, efficiency, switching cycles, operating hours, error messages and communication quality. Battery energy storage systems (BESS) can additionally provide measured values such as cell voltages and temperatures, as well as condition parameters calculated or estimated by the battery management system (BMS), such as state of charge (SoC), state of health (SoH) or available energy content.
The complexity of the analysis can vary significantly. Simple methods monitor thresholds or determine whether a measured value is developing in a critical direction over time. Statistical models compare current behaviour with historical data, comparable components or an expected operating profile. Machine-learning models can analyse more complex patterns and relationships but require suitable training data and careful validation.
An alarm or detected deviation is initially only an indication. Declining PV output, for example, may result from soiling, shading, cloud cover, ageing, a measurement error or a technical fault. Predictive maintenance should therefore consider the asset’s condition, its operating state and external influences together whenever possible.
Unlike calendar-based preventive maintenance, maintenance activities are not scheduled exclusively according to fixed time intervals. With condition-based maintenance, activities are triggered according to the asset’s currently determined condition. Predictive maintenance extends this approach by forecasting future condition developments, anticipated maintenance requirements or an increased probability of failure.
Typical Applications of Predictive Maintenance
In PV systems, predictive maintenance can identify declining string performance, unusual inverter temperatures, recurring communication failures or an increasing number of restarts. Comparing similar strings or inverters helps distinguish technical abnormalities from differences in operating conditions. Detecting these abnormalities is initially part of condition monitoring. The analysis becomes predictive maintenance when their development over time is used to determine future maintenance requirements or an increased risk of failure.
In BESS, relevant condition indicators can include determined capacity loss, an estimated increase in internal resistance, unusual temperature distributions and increasing cell-voltage differences. Which parameters are available and how reliably they can be determined depend on the BMS, its calculation methods and its communication interface.
Meters, sensors, switching devices and communication components can also be monitored. An increasing number of implausible measurements or connection failures may indicate sensor faults, network problems or unstable device interfaces.
Benefits, Limitations and Technical Requirements
Predictive maintenance can reduce unplanned downtime, improve the prioritisation of maintenance activities and support troubleshooting. Operators receive early indications of abnormal developments and can prepare inspections more effectively. Whether this actually reduces costs or downtime depends on detection quality, the criticality of the components and the available response options.
Reliable results require consistent measurement data, correct timestamps, appropriate sampling rates and a sufficient data history. Information about operating states and external influences is equally important. Without irradiance or temperature data, for example, it may be difficult to determine whether reduced PV output has a technical cause.
Missing data, changed operating conditions, faulty sensors or unsuitable reference models can cause false alarms or undetected faults. Rare failure modes are also difficult to predict when only a small number of comparable events are available. Critical warnings should therefore be supplemented by technical inspections and expert assessment.
Predictive Maintenance with EcoPhi
EcoPhi can collect and consolidate operational data from different components and make it available for historical analysis. Configurable thresholds, trend analyses, dashboards and alarms help make deviations and recurring abnormalities visible at an early stage. These functions do not constitute a failure prediction on their own, but they can provide the data foundation required for project-specific predictive maintenance methods.
Whether reliable failure or remaining-useful-life predictions are possible depends on data quality, the available data history, suitable reference models and the project-specific analysis function. The specific implementation also depends on the available device interfaces and measurements.
Predictive Maintenance in Summary
Predictive maintenance uses operational and condition data to identify future maintenance requirements as early as possible. It can improve asset availability and maintenance planning but does not provide automatic certainty about the cause of a fault. Reliable data, suitable prediction models and expert evaluation of the results are essential.
Frequently Asked Questions About Predictive Maintenance
What is the difference between predictive and preventive maintenance?
Preventive maintenance is often performed according to fixed time or usage intervals. Predictive maintenance, by contrast, aligns maintenance activities with the determined and predicted condition of an asset.
What is the difference between condition monitoring and predictive maintenance?
Condition monitoring records and evaluates the current condition of an asset. Predictive maintenance uses this information and its development over time to estimate future maintenance requirements or an increased risk of failure.
Does predictive maintenance always require artificial intelligence?
No. Trend analyses and statistical methods can also be used. Machine learning is particularly useful when sufficient high-quality data is available and complex patterns need to be identified.
Which data can be relevant for PV systems?
Depending on the analysis objective and system configuration, relevant data may include power, voltage, current, inverter temperatures, status messages and communication data. External factors such as solar irradiance and ambient temperature should also be considered when interpreting the results.