OPTIMIZING TRAIN HEALTH IN THE LONG RUN
Maintenance interval optimization
The Analytics software module calculates a statistical failure probability for every single component. These calculations can be used to optimize maintenance intervals based on component wear.
The accuracy of the predicted maintenance intervals increases based on the volume of analyzed data. Additional information, such as traveled distances (miles/kilometers) can be included to improve the accuracy even further.
HIGHLY EFFICIENT MAINTENANCE INTERVALS
Failure probability until the next maintenance interval
When known maintenance intervals are combined with existing analysis data, the Analytics software can calculate and display the failure probability for an entire train, for example, as color-coded diagrams, percentage values, trend plots, correlation tables and more. The accuracy can be increased by prioritizing or classifying particular components as operation-critical parts to prevent potential downtimes and related costs before they happen.
The Analytics software module can be used to evaluate the quality or deterioration of all monitored components. It enables maintenance crews to identify and systematically replace any wear- or failure-prone parts at the right time. This contributes to streamlining maintenance intervals, reducing time and spare part costs and leads to increased train availability.
A HOLISTIC APPROACH
Adding Performance-enhancing Parameters
In addition to the data records created a variety of external parameters can be used to increase failure prognosis accuracy, including those that directly impact the components' lifespan.
Parameters such as weather conditions, traveled distance, velocity or component replacement and maintenance intervals for individual trains can be particularly useful.
Using the cloud for optimized data management
Uploading train data to the DA-MI-KA cloud increases both data safety and availability. Train data and analysis results can be accessed at any time from any location. The cloud eliminates data constraints, provides a central storage location and optimized data management for huge data volumes. Data from various systems at different locations can be analyzed and used simultaneously to explicitly highlight trends or dependencies (data mining).
All benefits at a glance:
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