IBM Maximo Models for Electric Transformers logo

IBM Maximo Models for Electric Transformers

IBM Maximo Models for Electric Transformers logo
IBM Maximo Models for Electric Transformers logo

IBM Maximo Models for Electric Transformers


Certified for Red Hat Enterprise Linux

An Intelligent Analytics accelerator that empowers you effortlessly generate and deploy analytic models without coding. Calculate critical KPIs such as CO2 Equivalent Emissions, Harmonic Anomaly, Energy Loss, and Health Score. Transform your approach to asset management with proactive, data-driven decision-making, streamlined operations, longevity and sustainability of electric transformers.

Software version


Runs on

RHEL 7, 8

Delivery method


Estimate CO2 emissions with energy loss calculations and transformer location data using our CO2 Equivalent Emissions module. Implement effective, rule-based methods to detect harmonic anomalies in voltage Total Harmonic Distortion, triggering alerts for potential issues. Identify and address energy loss factors to enhance system efficiency and minimize environmental impact. Quantify transformer health and enable continuous monitoring for optimized operational efficiency and extended lifespan.

CO2 Equivalent Emissions

The CO2 equivalent estimation is used to estimate emissions based on the inefficiency of transformer electricity. The KPI can be utilized to help optimize sustainability for power companies by utilizing location-based transformer operation data, enabling precise estimation of global warming impact, and ensuring business decisions aligned with market demands and sustainability regulations.

Rule-based Harmonic Anomaly Detection

Rule-based anomaly detection utilizes the sensors' threshold set points to generate trigger alerts when observed values exceed safety thresholds; it further employs an additional aggregation approach to prevent anomaly flooding. It can apply to a wide range of anomaly detection, including but not limited to the Voltage Total Harmonic Distortion and cooling oil overheating.

Energy Loss

Real-time monitoring of the energy loss is critical for identifying the transformer inefficiencies. By estimating the energy loss and other IoT monitoring, we can identify the inefficiencies related to outdated transformers, capacity issues, and overheating cooling oil. This model can help the users to enhance overall efficiency, cut operation costs, and reduce environmental impact.

Transformer Health Score

Quantify the electrical transformer's health with a health score, typically ranging from 0 to 100. With the monitored DGA (Dissolved Gas Analysis), an estimated health score helps us identify health score trends and patterns, facilitate adjustments to maximize operational efficiency and extend the transformer's lifespan.

Anomaly Detection

Anomaly detection with a Machine Learning approach is crucial for identifying abnormal behavior from IoT signals rooted in potential faults or failures. Early detection of failure symptoms allows the users to adopt proactive preventative maintenance, to mitigate the risk of costly downtime or damage to the transformer.

IBM Maximo Application Suite Compatibility

Compatible with IBM Maximo Application Suite v8.10/v8.11 and all subsequent patches. Not currently compatible with IBM Maximo Application Suite as a Service.

Additional resources

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