Add additional information to the bottom of the Anomaly Detection - Spot Welding notebook.

[DallasBowden]

Please add a section at the end of existing demos to show how the analytic approaches can be generalized to other use cases/ industries.
Here is what I have written to add to the spotwelding anomaly detection demo on CSAE

How this analytic approach can be levaraged in other use case settings
The analytical approach of leveraging clustering followed by classification for anomaly detection in short time series data is highly adaptable and can be broadly applied across various industries, especially in settings where operations or processes are characterized by short, continuous time series with a defined start and end and where ground truth labels are not initially available.
This method begins with unsupervised learning to explore and understand the data, identifying patterns, similarities, and potential outliers through techniques like Dynamic Time Warping (DTW). Such exploration is crucial in settings where anomalies are not predefined or where the data’s inherent complexity requires initial unsupervised insight to develop an understanding of what constitutes normal behavior versus an anomaly. Following the clustering phase, supervised classification models are trained on the newly identified labels to predict anomalies. This generic approach is particularly effective for short time series data, where each sequence represents a process or event whose normal operational parameters need to be defined through exploratory analysis before precise anomaly detection can occur.
Potential Use Cases Across Industries:
• Telco & Utilities - Power Grid Load Monitoring: Analyzing short time series of electricity load during peak usage times to identify anomalies that could indicate equipment failure, energy theft, or inefficiencies in power distribution. Each series could represent the load profile for a brief, high-demand period.
• Healthcare - ECG or EEG Analysis: Short segments of electrocardiogram (ECG) or electroencephalogram (EEG) readings can be analyzed to detect anomalies indicating cardiac arrhythmias or neurological issues, respectively. Each segment represents a complete heartbeat or a brief brain activity pattern.
• Manufacturing - CNC Machine Operations: Monitoring the torque and force profiles of a CNC (Computer Numerical Control) machine during a single machining operation. Anomalies could indicate tool wear, material inconsistency, or operational errors.
• Travel & Transport - Aircraft Engine Test Runs: Analyzing the time series data of engine parameters (e.g., temperature, pressure, vibration) during short test runs to identify deviations from normal operational profiles, suggesting maintenance or safety issues.
• Hospitality & Entertainment - Theme Park Ride Operations: Analyzing sensor data from individual rides, where each ride cycle produces a time series of mechanical or operational parameters. Anomalies in these series could indicate safety concerns or maintenance needs.
Conclusion
In each of these scenarios, the focus is on analyzing the shape or behavior of a curve within a short time frame, similar to observing a spot welding curve. These curves are shaped by the specific activity taking place, whether it’s a machine at work, a health test running, financial trades happening, or people interacting with a service. The method begins by sorting these curves into groups based on their patterns, without needing to know ahead of time which ones are out of the ordinary. Then, it moves on to use a more detailed approach to pinpoint which curves don’t fit the expected pattern, labeling them as either normal or not normal. This way of doing things is great for quickly finding and addressing issues, and it also helps in getting a better grasp of how these processes work. This can lead to making things run more smoothly and keeping equipment in good shape before problems even start.