The Analytics Engine is an Utthunga ecosystem vendor product. The analytics engine is well integrated with Utthunga products (data acquisition, visualization, etc.). At its core, the analytics engine detects an anomaly in-process data to predict and prescribe. The goal is to enable the end customer to improve operation efficiency, increase uptime of assets and aid in real-time decision making.


Big Data is the enabler for the analytics technology. Big data enables collection and storing massive amounts of operational data generated in any process or discrete plant. The analytics engine inspects this massive amount of data to churn out the key predictive and prescriptive insights.


The Vector Quantization Clustering (VQC) and Local Subspace Classifier (LSC) algorithms perform machine learning on the raw process data. These algorithms inspect the differences between the actual data and the learned normal data. The engine then evaluates if the difference is within the allowed limits or not.


The analytics engine employs a core set of proprietary algorithms. Traditional algorithms such as the Mahalanobis-Taguchi (MT) can only be applied when data has a normal distribution. The proprietary algorithms that have been developed are resistant to the effects of the data distribution. Since the algorithms are model-free, they can respond flexibly without the need for model construction or simulations for each device or operation status change. The engine has been designed to simplify the cause analysis by outputting an ordered list of the parameters responsible for a detected status change.


Strong data acquisition capability from varied data sources and other software systems as well if required. Utthunga’s capabilities here ensures legacy, as well as dumb devices, don’t get left out.


Historical data is used to create a powerful data model, and the analytics is based on streaming/real-time data. Proprietary and standard algorithms churn out predictive and prescriptive analytics.


A simple user interface to view the generated insights. A SCIR table is generated which gives the information related to Symptom/Cause/Impact and Remedy in textual format.

Key benefits of the analytics engine are:

  1. Reduce maintenance cost
  2. Reduce unplanned downtime and production loss
  3. Increase machine life
  4. Reduce reliance on expensive process specialists/engineers
  5. Get prescriptive input to correct process.