Introduction to Rapid Process Troubleshooting

The Rapid Process Troubleshooting Methodology provided in the Continuous Wizard follow these seven steps as provided on the project bar:

1. Data Preparation

2. Visualization

3. Modeling

4. Knowledge Extraction

5. Benefit Estimation

6. Knowledge Fusion

7. Action Deployment

Step 1: Data Preparation

Data preparation is the first step required in order to prepare your data for modelling. Data is imported, and mathematical, statistical and dataset manipulation operations are applied to the data. Once the data source is properly configured, load the dataset into the Troubleshooter project, and continue with visualizing the data.

Step 2: Visualization

During this step, data is visualized and prepared for modeling.  Visual and statistical techniques are used to prepare the data that will be used in the following steps to build models. Thus it is important to use good quality data as well as data that is an accurate representation of the process.  At this stage users should:

1. Set limits for process variables

2. Decorrelate the data set using a correlation matrix

3. Visualize and explore the data set using trends, histograms and scatter plots with the powerful history brushing capabilities to identify trends and clusters in the data set.

4. Identify cyclical events in your data using powerful frequency analysis displays.

Step 3: Modeling

Powerful non-linear and rule-driven process models are constructed from the process data. The rule-based model builds a set of if-then rules with the selected target as the outcome and the input fields as adjustables.

Step 4: Knowledge Extraction

Process models constructed during the modeling step are harnessed using an intuitive troubleshooting dashboard to extract new process knowledge from the data.  The following techniques and information is provided in order to facilitate knowledge extraction:

• Causal analysis

• Input/Output (IO) Analysis

• Rule-based analysis

• Process simulation

• What-if scenario analysis

• Leverage (sensitivity) analysis

Step 5: Benefit Estimation

This step helps the user to estimate the benefit of knowledge gained from the previous step in applying ranges and set points to input fields will have.

Step 6: Knowledge Fusion

In this step a process action model is constructed to realize the benefits estimated in Step 5. The constructed action model can then be exported to be further modified and expanded in the Architect.

Step 7: Action Deployment

This step allows for exporting the following from the Continuous Troubleshooter:

• Action Object blueprint
  
  This blueprint can be loaded into the Architect and expanded into a full Action Object solution.

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Next topic:

• Data Preparation