- Conduct diverse data analyses using Python/MATLAB for a process objective.
- Seeking help for a basic linear method and an advanced method.
- You are considering modelling a water storage tank with 2 pumps and 2 valves aimed at maintaining set point.
- You want to begin by modelling the water tank system using differential equations to generate data representing normal operating conditions.
- You want to simulate faults by introducing biases in sensor data, reflecting sensor behaviour under faulty conditions.
- Address the challenge of accurately modelling sensor biases to realistically represent their behavior in a faulty scenario.
- Explore how regression techniques can be applied to predict water levels in the absence of direct measurements.

- Begin the project by focusing on modelling a water storage tank system using Simulink. Here’s how you can do this https://in.mathworks.com/help/slcontrol/gs/watertank-simulink-model.html
- Kindly utilize differential equations to precisely describe the dynamics of the system, particularly the rate of change of water level in the tank.
- To simulate faults, you can introduce biases to sensors in your Simulink model. For instance, you can add biases to the level sensors to mimic faulty data.
- Adjust sensor readings to represent different fault scenarios, such as sensor bias or inaccuracies. Model sensor bias by adding an offset to the sensor readings. For example, if the actual water level is 100 units, introduce a bias that might make the sensor report 90 units.
- Simulate the system with introduced biases to generate faulty data. This data can be used to train and test your fault detection algorithm.
- If you lack actual measurements for the water level, you can use regression techniques to predict it based on other available data. Collect various parameters from your system (e.g., pump speeds, valve positions) as input features and the water level as the output variable for the regression model.
- Implement your data analysis and regression techniques in MATLAB. Use MATLAB's regression functions to train your model on the normal operating data and test its predictive capabilities on the faulty data. Here is an example of how to do it https://in.mathworks.com/help/stats/train-linear-regression-model.html
- For a basic linear technique, kindly consider using simple linear regression in MATLAB to predict the water level based on selected parameters.
- For a more advanced technique, kindly use MATLAB’s built-in functions for creating and training neural networks. Here is how you can do this https://in.mathworks.com/help/deeplearning/ref/trainnetwork.html