Modelling Time Series for Condition-Based Monitoring

Errors can often be seen before they occur. The challenge is to recognize these signs in time while keeping the effort low.

Read here how Verbund, Austria’s largest electricity producer, used Visplore to develop an early fault detection rule for a hydropower turbine.

Read User Success Story

Watch this short demo and learn how easy it is to build a multivariate regression model for predicting a turbine’s normal expected oil temperature from raw sensor data within minutes.

CONDITION-BASED MONITORING
INTUITIVE MODELLING FOR EARLY FAULT DETECTION

Implement condition-based monitoring of your assets as fast and easy as never before! Visplore Professional covers all steps with intuitive graphical tools – data preparation, sensor modelling, monitoring using dynamic tolerance bands, and root-cause analysis. This enables to detect and understand problems of your assets earlier – for reliable operation.

Save much time for validating and preparing your data for modelling
Model sensor behavior by multivariate regression without programming skills
Define dynamic tolerance bands based on the modelled behavior
Compare models for informed selection

Investigate tolerance violations in detail for identifying root causes
Detect tolerance violations, anomalies and drifts for hundreds of sensors at a glance
Use these insights to act earlier and to optimize your system as well as your maintenance intervals

Use multivariate regression to model the expected behavior of your assets.
Click Image to enlarge

Static tolerances often need to be wide (left) while dynamic tolerances can be narrower without increasing the fault positive rate (right): Actual anomalies can thus be detected earlier.
Click Image to enlarge

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If you want to learn more, or discuss an application for your individual data, feel free to book a live demo!

MODELLING FOR CONDITION-BASED MONITORING WITH VISPLORE

Condition-based monitoring (CBM) is an aspect of predictive maintenance. CBM allows to detect faults ahead of time and to optimize maintenance intervals. The challenge lies in dealing with large, complex operating data that may not be fit for direct use, e.g., due to outliers or abrupt changes. The knowledge and experience of the plant engineer is needed to interpret the raw data, to correct it, and to annotate it the following tasks.

The path from the raw data to obtaining a rule for early fault detection involves several steps. First, the data must be checked for plausibility, and suitable parts of the data must be identified for further processing. With this, the plant behavior can be modeled. A model “learns” the normal, desired state of the plant from the historical data, enabling to detect deviations from the expected state during operation. Further findings, such as wear processes between revisions, can be investigated and optimized during the modeling process.

Visplore supports the plant engineer in all these steps. Thanks to predefined views and tools, the plant expert can inspect and prepare the raw data, model it, develop a rule for early warnings, and validate that rule based on actual data.

READ USER SUCCESS STORY

Why our customers
prefer Visplore

Perfect for dirty sensor data. With Visplore, we give customers more insights for the same money.

Research Engineer, R&D Consultancy Firm

When we need it fast, we use Visplore.

Data Scientist, Hydropower Company

Visplore enabled us to gain significant new insights from massive data about the impact of production process parameters on product quality.

Process Expert, Refractories Enterprise

Thanks to its ease of use and high performance even with millions of measurements, Visplore has established itself as our standard tool for quality management in plants from China to South America.

Head of R&D, Refractories Enterprise

Checking and cleaning data from energy systems used to be very time-consuming and kept us from our core tasks. With Visplore, data preparation has become much faster, and the quality of the resulting data has improved.

Leader of Energy Innovation Lab

The good usability and the comprehensive visualizations enabled us to gain a deeper understanding of routinely collected data and their relationships. For example, this increased the quality of forecast models.

Expert Renewable Energy, Transmission System Operator

Visplore is recognised and in use as a central strategic software for data analytics throughout the company.

Coordinator of Renewable Energy Data, Transmission System Operator

By analyzing process data from a large foundry with Visplore, complex dependencies were detected that we didn’t find with other software. As a result, our customer’s heat energy was reduced by around 5% and mold wear by around 4%.

Process Expert, Foundry Research Institute

For our thermodynamical simulations, Visplore helps us to identify suitable data, eliminate outliers, and analyze physical processes very effectively.

Manager Process Engineering, Thermal Energy Company

The visualization is fast and simple enough for live collaboration with the customers, on site. By this, Visplore supports a thorough understanding of correlations, outliers, and anomalies.

Head of Research in Industrial Data Science, R&D Research Institute

Visplore makes our analysis of sustainable energy systems much more efficient.
In addition to the performance for large data, its integration with scripting environments is a big plus.

Research Engineer, Energy Research Center

Visplore allows for a much easier investigation of relevant patterns and structures. Our downstream analysis becomes more efficient and we gain more confidence in the results.

Main Data Scientist, Hydropower Provider

Modelling Time Series for Condition-Based Monitoring

CONDITION-BASED MONITORING
INTUITIVE MODELLING FOR EARLY FAULT DETECTION

If you want to learn more, or discuss an application for your individual data, feel free to book a live demo!

MODELLING FOR CONDITION-BASED MONITORING WITH VISPLORE

Why our customers
prefer Visplore

Detect faults as fast and easy as never before.

Why Visplore?

Resources

Newsletter

Modelling Time Series for Condition-Based Monitoring

CONDITION-BASED MONITORINGINTUITIVE MODELLING FOR EARLY FAULT DETECTION

If you want to learn more, or discuss an application for your individual data, feel free to book a live demo!

MODELLING FOR CONDITION-BASED MONITORING WITH VISPLORE

Why our customersprefer Visplore

Detect faults as fast and easy as never before.

Why Visplore?

Resources

Newsletter

CONDITION-BASED MONITORING
INTUITIVE MODELLING FOR EARLY FAULT DETECTION

Why our customers
prefer Visplore