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Name of Company: CSense Systems (Pty) Ltd

Name of Project: Discrete Troubleshooting

Project Description:

CSense (Pty) Ltd (CSense) is an engineering technology company based in Waterkloof Heights, Pretoria. The company’s focus is the development and supply of platforms for advanced control and process solutions.

In any process-driven industry, there is a need for tools to monitor and control all steps in the end-to-end process. With the advent of advanced industrialisation, whether dealing with raw materials or processing further down the supply chain, all aspects of the process must be monitored and controlled to achieve the desired end state. Advanced process control systems and technologies are generally responsible for collecting data relevant to the process, monitoring and analysing all variables and events and recommending solutions to ensure that productivity and quality are maintained at correct levels to enable maximised output according to a designated desired end state.

Advanced process control solutions involve troubleshooting and optimising key performance indicators such as quality, throughput, energy consumption, control loop effectiveness, and equipment health, as well as providing real-time predictors, intelligent alarms, set-point advisory systems, and expert and model-predictive control systems.

CSense for Discrete Troubleshooting is specifically geared towards supporting the troubleshooting process on discrete data of production or manufacturing processes. CSense Discrete Troubleshooter does not assume advanced statistical background of users and operators, but focuses on facilitating the discrete troubleshooting process from data to action in an easy to use manner. The software allows users to import and merge multiple discrete and continuous data sources, do knowledge discovery via a decision tree driven analysis and visualisation process, make an on-demand scheduled or real-time process dashboard available for process analysis and comparison of the latest process with the process model, fusing knowledge obtained from the data-driven knowledge discovery process with existing expert knowledge by incorporating an action model, and generating an action dashboard for scheduled or real-time deployment.