[DOCS] time series modular refactoring

manufacturing-ai-suite/industrial-edge-insights-multimodal/docs/user-guide/index.md

@@ -1,4 +1,4 @@
-# MultiModal Weld Defect Detection
+# Industrial Edge Insights - Multimodal
 
 MultiModal sample apps demonstrate how to use AI at the edge to identify defects in manufacturing environments by analyzing both image and time series sensor data.
 
@@ -26,14 +26,16 @@ Multimodal, real-time monitoring of weld defects.
 hide_directive-->
 
 <!--hide_directive
-.. toctree::
-   :hidden:
-
-   weld-defect-detection/how-it-works.md
-   weld-defect-detection/system-requirements.md
-   weld-defect-detection/get-started.md
-   weld-defect-detection/how-to-build-from-source.md
-   weld-defect-detection/how-to-configure-alerts.md
-   weld-defect-detection/how-to-update-config.md
-   weld-defect-detection/release_notes/Overview.md
+:::{toctree}
+:hidden:
+
+weld-defect-detection/how-it-works.md
+weld-defect-detection/system-requirements.md
+weld-defect-detection/get-started.md
+weld-defect-detection/how-to-build-from-source.md
+weld-defect-detection/how-to-configure-alerts.md
+weld-defect-detection/how-to-update-config.md
+weld-defect-detection/release_notes/Overview.md
+weld-defect-detection/Overview.md
+:::
 hide_directive-->

manufacturing-ai-suite/industrial-edge-insights-time-series/README.md

@@ -6,19 +6,30 @@ Industrial Edge Insights - Time Series sample application demonstrates a time se
 
 To see the system requirements and other installation, see the following guides:
 
-  - [System Requirements](docs/user-guide/system-requirements.md): Hardware and software requirements for running the sample application.
-  - [Get Started](docs/user-guide/get-started.md): Step-by-step guide to getting started with the docker compose deployment of the sample application.
+  - [System Requirements](docs/user-guide/wind-turbine-anomaly/system-requirements.md): Hardware and software requirements for running the sample application.
+  - [Get Started](docs/user-guide/wind-turbine-anomaly/get-started.md): Step-by-step guide to getting started with the docker compose deployment of the sample application.
 
 ## Architecture and Functionality Overview
 
-Refer [How it works](docs/user-guide/how-it-works.md).
+The Industrial Edge Insights - Time Series sample application comprises of data simulators, the generic Time Series AI stack based on **TICK Stack**, and Grafana. The Model Registry microservice helps to achieve the MLOps flow by uploading the **UDF deployment package**.
+
+![Time Series AI Stack Architecture Diagram](./docs/user-guide/wind-turbine-anomaly/_images/time-series-ai-stack-architecture.png)
+
+  - **Data Simulators/Destinations**: OPC-UA server and MQTT Publisher simulate data sources and destinations, reading from CSV files and interfacing with Telegraf plugins for data ingestion.
+  - **Generic Time Series AI Stack**: A customizable pipeline for data ingestion, storage, processing, and visualization, supporting integration with various databases, and enabling deep learning model execution.
+  - **Data Ingestion**: Telegraf collects and reports metrics using input plugins, sending ingested data to InfluxDB for storage.
+  - **Data Storage**: InfluxDB is a high-performance database optimized for time series data, supporting high write throughput and efficient querying.
+  - **Data Processing**: Kapacitor processes time series data in real-time, allowing custom logic using User-Defined Functions (UDFs) for anomaly detection and advanced analytics.
+  - **Data Visualization**: Grafana offers an intuitive interface for real-time visualization of time series data stored in InfluxDB, enabling custom dashboards and monitoring.
+
+For more details on Architecture, see [How it works](docs/user-guide/wind-turbine-anomaly/how-it-works.md).
 
 ## Learn More
 
-  - [How to build from source and deploy](docs/user-guide/how-to-build-from-source.md): Guide to build from source and docker compose deployment
-  - [How to configure OPC-UA/MQTT alerts](docs/user-guide/how-to-configure-alerts.md): Guide for configuring the OPC-UA/MQTT alerts in the Time Series Analytics microservice
-  - [How to configure custom UDF deployment package](docs/user-guide/how-to-configure-custom-udf.md): Guide for deploying a customized UDF deployment package (udfs/models/tick scripts)
-  - [How to create a new sample app](docs/user-guide/how-to-create-a-new-sample-app.md): Guide for creating a new sample app by referencing Wind Turbine Anomaly Detection sample app
-  - [How to connect to secure MQTT broker](docs/user-guide/how-to-connect-to-secure-mqtt-broker.md): Guide for connecting to secure MQTT broker.
-  - **Release Notes**
-    - [Release Notes](docs/user-guide/release_notes/Overview.md): Information on the latest updates, improvements, and bug fixes.
+  - [How to Deploy with Helm](docs/user-guide/wind-turbine-anomaly/how-to-deploy-with-helm.md): Guide for deploying the sample application on a k8s cluster using Helm.
+  - [How to build from source and deploy](docs/user-guide/wind-turbine-anomaly/how-to-build-from-source.md): Guide to build from source and docker compose deployment
+  - [How to configure OPC-UA/MQTT alerts](docs/user-guide/wind-turbine-anomaly/how-to-configure-alerts.md): Guide for configuring the OPC-UA/MQTT alerts in the Time Series Analytics microservice
+  - [How to configure custom UDF deployment package](docs/user-guide/wind-turbine-anomaly/how-to-configure-custom-udf.md): Guide for deploying a customized UDF deployment package (udfs/models/tick scripts)
+  - [How to create a new sample app](docs/user-guide/wind-turbine-anomaly/how-to-create-a-new-sample-app.md): Guide for creating a new sample app by referencing Wind Turbine Anomaly Detection sample app
+- **Release Notes**
+  - [Release Notes](docs/user-guide/wind-turbine-anomaly/release_notes.md): Information on the latest updates, improvements, and bug fixes.

manufacturing-ai-suite/industrial-edge-insights-time-series/docs/user-guide/index.md

@@ -0,0 +1,58 @@
+# Industrial Edge Insights - Time Series
+
+Time Series predictive maintenance apps allow for detecting anomalous patterns across time,
+such as power generation patterns relative to wind speed for the wind turbines.
+
+In the Energy Sector unexpected equipment failures result in costly downtime and operational
+inefficiencies. Using AI-driven predictive analytics, edge devices can monitor equipment
+health through sensor data (for example, power output) detect anomalous trends indicative
+of wear or failure, and alert operators to schedule maintenance proactively.
+This enhances productivity, reduces costs, and extends equipment lifespan.
+
+[Wind turbine anomaly detection](./wind-turbine-anomaly/user-guide/get-started.md) sample app
+demonstrates a time series use case by detecting anomalous power generation patterns
+in wind turbines, relative to wind speed. By identifying deviations, it helps
+optimize maintenance schedules and prevent potential turbine failures, enhancing
+operational efficiency.
+
+[Weld Anomaly Detection](./weld-anomaly-detection/index.md) sample app demonstrates how AI-driven analytics enable edge devices to monitor weld quality.
+They detect anomalous weld patterns and alert operators for timely intervention,
+ensuring proactive maintenance, safety, and operational efficiency. No more failures
+and unplanned downtime.
+
+<!--hide_directive
+::::{grid} 1 2 3 4
+:::{grid-item-card} Wind Turbine Anomaly Detection
+:class-card: homepage-card-container-big
+:link: ./wind-turbine-anomaly/get-started.html
+
+Monitoring power generation anomalies for preventive maintenance.
+:::
+:::{grid-item-card} Weld Anomaly Detection
+:class-card: homepage-card-container-big
+:link: ./weld-anomaly-detection/index.html
+
+Monitoring weld anomalies for preventive maintenance.
+:::
+::::
+hide_directive-->
+
+<!--hide_directive
+:::{toctree}
+:hidden:
+
+wind-turbine-anomaly/how-it-works.md
+wind-turbine-anomaly/system-requirements
+wind-turbine-anomaly/get-started
+wind-turbine-anomaly/how-to-build-from-source
+wind-turbine-anomaly/how-to-deploy-with-helm
+wind-turbine-anomaly/how-to-configure-custom-udf
+wind-turbine-anomaly/how-to-configure-alerts
+wind-turbine-anomaly/how-to-enable-system-metrics
+wind-turbine-anomaly/how-to-update-config
+wind-turbine-anomaly/how-to-create-a-new-sample-app
+wind-turbine-anomaly/how-to-connect-to-secure-mqtt-broker
+wind-turbine-anomaly/release_notes
+weld-anomaly-detection/index
+:::
+hide_directive-->

manufacturing-ai-suite/industrial-edge-insights-time-series/docs/user-guide/weld-anomaly-detection/index.md

@@ -0,0 +1,3 @@
+# Weld Anomaly Detection Sample App
+
+(work in progress)

manufacturing-ai-suite/industrial-edge-insights-time-series/docs/user-guide/how-to-connect-to-secure-mqtt-broker.md → manufacturing-ai-suite/industrial-edge-insights-time-series/docs/user-guide/wind-turbine-anomaly/how-to-connect-to-secure-mqtt-broker.md

@@ -155,7 +155,7 @@ Edit your Telegraf configuration file:
 [[inputs.mqtt_consumer]]
   ## MQTT broker URLs - use ssl:// for secure connection
   servers = ["ssl://<YOUR_MQTT_BROKER_IP>:<MQTT_PORT>"]
-  
+
   ## TLS Configuration
   tls_ca = "/run/secrets/ca_certificate.pem"
 
@@ -175,10 +175,10 @@ Edit the `kapacitor.conf` file:
   enabled = true
   name = "my_mqtt_broker"
   default = true
-  
+
   # Use SSL connection
   url = "ssl://<YOUR_MQTT_BROKER_IP>:<MQTT_PORT>"
-  
+
   # TLS/SSL configuration
   ssl-ca = "/run/secrets/ca_certificate.pem"
 ```