Use Case Library

Solution Template

Wind Turbine

Wind turbines have become progressively more influential as the share of energy production and the infiltration of wind energy into power systems are steadily increasing. With this, the need for reliability in the production capacity of wind turbines has increased as well. The turbines must operate as smoothly as possible, since the unscheduled stoppage of these turbines can lead to significant production losses. In this use case the importance of operations and predictive maintenance are highlighted, and especially the role of health monitoring. Continuous monitoring of wind turbine health using anomaly detection improves turbine reliability and efficiency thus reducing maintenance and wind power costs. Finally, it allows for the optimal timing of turbine maintenance and repairs so that they can reduce the impact on the overall energy production and avoid catastrophic failure of the turbines.

TIM’s Value

Due to the highly automated, exceptionally fast and reliable modeling algorithm, TIM can build multiple anomaly detection models in a limited amount of time. It is especially useful in this case, since wind turbines often operate in wind farms where multiple turbines need to be monitored simultaneously. The speed and frequency of model building that TIM is capable of also allows for real time notifications of suspicious behavior in any turbine.

Data

Building a model for the detection of anomalous behavior in wind turbines requires a set of training data with several variables. The power output of a wind turbine is dependent on the efficiency of the blades, gear assembly, alternator/dynamo, as well as wind speed, wind direction and wind consistency. Also, the taller the wind turbine, the greater the energy produced, since wind speeds are greater at higher altitudes. With these variables set up in a time series format, TIM can use its anomaly detection capabilities to determine whether or not a power output observation is abnormal.

More Information

Are you interested in a walk-through scenario of this type of use case? Then take a look at our solution template on this use case! You can find it under Wind Turbine.

Author:

Carl Fransman - Tangent Works

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Transportation

Manufacturing & Natural Resources

Manufacturing & Natural Resources

Asset Failure Analysis

Complex and distributed assets (i.e. differently configured pumps or compressors installed across the globe) fail because of many reasons; some are purely due to the conception of the asset and represent normal wear and tear. Some failures though are due to local operating conditions and/or the specific configuration of the asset. Gathering data through IIoT platforms and performing anomaly detection not only allows for foreseeing such failures, but when this anomaly detection leads to explainable forecasts, engineers can perform root cause analysis. This leads to faster resolution of the issue and also allows R&D to analyse failures and come up with more robust and reliable equipment, which is even more important under servicisation-type contracts where the manufacturer bears (some of) the cost for maintaining the equipment and guaranteeing uptime.

TIM’s Value

TIM’s forecasting and anomaly detection capabilities not only produce accurate results, but these results are fully explainable; therefore TIM’s value extends beyond avoiding the failure and supporting predictive maintenance. TIM’s information can be analysed by technical maintenance teams in order to pinpoint the culprit rapidly and thus save precious production time by limiting downtime. TIM’s information can also be analysed by R&D teams to determine structural improvements to the equipment.

Data

Typical datasets used in this use case, consist of CMMS data combined with IIoT data and potentially external elements, such as operating conditions (weather, vibrations, speed, etc.)

Author:

Philippe Thys - Tangent Works

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Asset Health Monitoring

Getting the most out of your production assets, especially with constraints, is the foundation of increasing the flow of profits through your production lines. The proliferation of time-stamped data follows naturally from the digitisation of industry. The ongoing deployment of billions of connected sensors will only accelerate the trend. As a consequence, lots of decision-making processes that used to be fairly static (based on stable information) are becoming dynamic (based on streaming data). Highlighting abnormal patterns directly from multi-variate sensor readings to help with inspection and diagnoses through anomaly detection in time-series data (generated on top of failure codes returned from PLC and SCADA systems) will help to alert for potential equipment failures during the production runs. These signals can then be analysed and used as indicators for potential performance degradation or equipment failure. Time series machine learning differs significantly from standard machine learning practices, and many current machine learning solutions applied to time series underperform and are not agile enough to react to the dynamics of the new data inflows.

TIM’s Value

Increase your return on assets with TIM’s anomaly detection capabilities by reducing unplanned maintenance and increasing equipment uptime. The ease of use, speed of setting up and generating trained models, together with a very fast AI engine enables companies to implement near real time anomaly detection at an unprecedented scale. Users can now create and deploy models at field level (sensors) as well as direct control and plant supervisory levels. They will be able to create and maintain Machine Learning capabilities that keep up with the dynamics of their enterprise.

Data

Typical input data in this use case consists of raw time series data from PLCs, SCADA and sensor data, such as vibration, temperature, revolutions, pressure, quality, etc… TIM then returns the detected anomalies to the user, consisting of anomalies on component, subcomponent, machine, and/or production line level.

Author:

Carl Fransman - Tangent Works

Tags:

Transportation

Rolling Stock

Track-operated transportation system (metropolitan or passenger and freight rail) failures can be very expensive; from merely causing a delay (often blocking a track to follow-on traffic) to derailments. En-route failures need to be avoided at all costs for both safety and economic reasons. Predicting failures is complex though, because of a high degree of customisation among rolling stock and because the system is impacted by varying factors such as load and weather.

TIM’s Value

All-in predictive maintenance roll-out requires a huge upfront investment in systems and change management. TIM’s extremely fast approach to generating predictions permits rail and track operators to roll out predictive maintenance approaches one use case at a time, which reduces organisational stress due to change management (actually, once initial cases have proven value, teams typically demand to be next!), but also leads to very rapid ROI. This means projects can be kickstarted top-down as well as bottom-up. The low initial investment in order to prove the value of AI/ML through TIM allows users to put together a business case based on the actual impact on the business.

Data

TIM typically runs on top of a data and/or IoT platform and connects through an API for automated data ingestion. This can include schedules, sensor data, load data (passengers or cargo load), weather data, etc. Forecasted failures are typically fed to a service planning system or CMMS for planning preventive maintenance.

Author:

Carl Fransman - Tangent Works

Tags:

Manufacturing & Natural Resources

Data Center HVAC

Data centers are critical infrastructure for countless operations. HVAC (Heating, Ventilation and Air Conditioning) failures can lead to a partial or full shutdown of the data center infrastructure in order to avoid critical equipment destruction. These shutdowns can cost hundreds of thousands of dollars in service and repair and in missed SLA fines. The ability to timely forecast HVAC malfunctions allows for predictive maintenance intervention, which can be planned during off-peak hours and allows for better system balancing during the intervention.

TIM’s Value

TIM’s approach to anomaly detection leads to high accuracy forecasts, because TIM deploys the optimal model for each situation; i.e. a different model may be required at 2 AM compared to 2 PM. TIM not only provides an anomaly detection, but also explains what leads to this result; feeding this information back to the technical teams empowers them to rapidly pinpoint what will cause a failure and take appropriate evasive action.

Data

Typical data for this use case relates to power consumption, sensor data (i.e. DeltaP) from the HVAC and filter age, among others. Data from external sources is also often included, such as weather data.

Author:

Daniel Parton - Bardess

More info

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IoT Instrument Failures

The recent evolution of Internet of Things (IoT) technologies has resulted in the deployment of massive numbers of sensors in various fields, including manufacturing, energy and utilities, and logistics. These sensors produce huge amounts of time series data, but understanding the data generated and finding meaningful patterns remains an obstacle to successful IoT implementations.

A common problem that can be solved with IoT data is anomaly detection, where temporal patterns in the data are used to identify mechanical or electronic anomalies in a piece of equipment, prior to the occurrence of a failure. This approach can help to minimize downtime for manufacturing pipelines or other IoT networks, thus preventing potential blocks on revenue streams. It can also enable cost savings by allowing maintenance interventions to be scheduled only when necessary.

TIM’s Value

Machine learning techniques provide an ideal solution for solving anomaly detection problems. However, they are typically time-consuming and costly to implement. TIM provides a revolutionary solution to this problem by allowing the development of rigorous anomaly detection models with minimal lead time. This is due to its highly automated and exceptionally fast modeling algorithm.

Due to its speed, TIM’s anomaly detection can easily be applied at scale, to huge numbers of IoT instruments. In addition, the TIM algorithm is extremely lightweight, and can thus be run directly on edge devices, reducing the need for costly network communication.

Finally, TIM’s API-first infrastructure makes it simple to integrate models into a production workflow.

Data

Anomaly detection can be performed on a single instrument output data field, or it can combine information from multiple fields. For example, the information from a number of manufacturing instruments might be used to predict a quality metric for a material being produced. Or multiple data points from a single instrument might be used to predict when failure is likely to occur.

Author:

Daniel Parton - Bardess

More info

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Services

Marketing and Advertising

All efforts in marketing and advertising today rely on a wide array of data sources, usually including both internal and external datasets. While this allows for deep insights and highly efficient marketing campaigns, it can also cause problems.

Imagine you are analyzing an ad campaign, when you realize the number of impressions being delivered per day dropped dramatically at a certain date, two weeks ago. A frantic investigation reveals that something has changed in the external data source being used to target potential customers, but the vendor had not alerted you to this. This could equally affect a customer’s insights or segmentation project.

TIM’s Value

Using machine learning techniques for anomaly detection, you could have detected this ahead of time, instead of discovering the problem weeks or months down the line. However, implementing such a system from scratch requires much time and specialized expertise.

TIM provides a much-needed new approach to this problem, by making it possible to implement robust anomaly detection routines with minimal lead time. Firstly, it is highly automated, meaning no data science experience is required to build effective models. Secondly, the model training process is stunningly fast, taking only a few seconds for a typical dataset – this makes it very easy to build effective models and also allows for huge scaling possibilities. Finally, the API-first infrastructure makes it simple to integrate models into a production workflow.

TIM’s anomaly detection capabilities rest upon first defining “normal behavior” for a given variable or data field (achieved using the TIM forecasting model) and then extending that with an “anomalous behavior” learning algorithm.

Ultimately, an anomaly detection platform with TIM at the center can provide organizations with much-needed confidence in the data that is fundamental to their operation.

Data

TIM’s anomaly detection capabilities can be exploited using a single input variable – for example, if you want to detect anomalies for 1,000 fields from an external data source, you could build one model for each field. It can also be achieved when using multiple input variables. For example, you might want to detect anomalies in conversions, using inputs such as numbers of impressions across multiple marketing channels, economic metrics and more. TIM can handle both types of anomaly detection problems smoothly.

Author:

Elke Van Santvliet - Tangent Works

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