Smart Asset Management Analytics

Aricent enables its clients to develop analytics-based solutions that help in improving reliability and efficiency of clients’ assets, while minimizing failures and maintenance costs. Aricent’s asset management solution framework can be leveraged to build analytics-based asset monitoring and management solutions. Our framework uses analytics and big data technologies for collecting, validating and storing the data received from varied sources such as, smart meters, system sensors and other smart devices. The framework enables running descriptive, predictive and prescriptive analytics over the collected data. It also helps generate dashboards, reports and charts that provide insights to address critical business challenges.

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Smart Asset Management Analytics

Aricent enables its clients to develop analytics-based solutions that help in improving reliability and efficiency of clients’ assets, while minimizing failures and maintenance costs. Aricent’s asset management solution framework can be leveraged to build analytics-based asset monitoring and management solutions. Our framework uses analytics and big data technologies for collecting, validating and storing the data received from varied sources such as, smart meters, system sensors and other smart devices. The framework enables running descriptive, predictive and prescriptive analytics over the collected data. It also helps generate dashboards, reports and charts that provide insights to address critical business challenges.

Asset Management Solution Framework

The framework is developed over Hadoop platform and utilizes array of technologies such as Flume, Storm and Hive for collecting, validating and storing the streaming data.R programing language libraries are used for the predictive and prescriptive analytics to determine current state of the assets and predict asset failures. The dashboards and reports are developed using Tableau analytical software platform. The current solution accelerator works for the electrical utility infrastructure and uses the transformer data for building the analytical models. The platform can be further extended to support any engineering asset or hardware in the clients’ infrastructure.

Features

The key features are:

  • Streaming Data Handling: The REST/XML machine data received is converted into objects and passed to a Flume based workflow that streams the data to the Flume server, where it is recorded and documented after periodic time intervals to files. These files are then loaded to HDFS and stored into Hive data warehouse.
  • Alarm Generation: Storm-based Complex Event Processing (CEP) engine processes the asset’s data and generates alarm related to the overall asset’s health. The alarms are displayed on the user interface and also stored to the database.
  • Predictive Analytics: A Support Vector Machine (SVM) Model based on R is built for performing predictive data analytics using the data stored in database. SVM is supervised learning technique for classification and regression analysis. Periodically, a scheduled process retrains the R-Model by fetching the training data from Hive. The current SVM implementation focuses on a model for predicting the health of the engineering asset and classifying whether the asset is likely to fail or not.
  • Online Analytics: The incoming data is analyzed against the trained R-Model and the asset health related analyzed output is passed to the Web UI for display.
  • Visualization: Visually rich, interactive dashboards, reports and charts are generated using the Tableau analytical and visualization platform. The framework also provides an option to build visualization layer using HTML5 and angular.js. These reports provide the user a high level view on the asset's health and alarms are automatically generated in the system. The user can drill down further to get specific details.

Benefits

  • Gain actionable insights
  • Gain competitive advantage
  • Mitigate risks effectively
  • Extensive product engineering experience
  • Domain-specific solutions 
  • Quicker time-to-market
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