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Splunk Essentials for Predictive Maintenance

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Overview
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#WELCOME TO SPLUNK ESSENTIALS FOR PREDICTIVE MAINTENANCE

This Splunk Essential for Predictive Maintenance is created for maintenance reliability leaders and engineers who are looking for ways to improve and optimize their current preventive maintenance program. To do predictive maintenance right, you need to have the right amount of knowledge on how to process data and then apply machine learning techniques to achieve the desired results.

Designed for interactive learning, this guide focuses on learning how to solve real problems with real data. Users are provided with details of the predictive maintenance methodology and performance data from airplane engine sensors and then asked to operationalize their analytics.

The Splunk Essentials for Predictive Maintenance takes you through 4 stages of the analytics process. Each stage covers :
* Methodology - designed to cover essential analytics knowledge to practice predictive analytics
* Step-by-step - designed to apply the knowledge to already included dataset.

- Stage 1: Data collection and ingestion focuses on how you can easily use Splunk software to collect, store, and structure asset metrics (dataset from airplane jet engines included).
- Stage 2: Data exploration covers key methods for pre-processing and exploring data to help you understand the type of data in use and the characteristics of the dataset, which is crucial for getting the desired outcome.
- Stage 3: Analysis teaches you 3 key analysis options (Anomaly Detection, Unsupervised Learning, Supervised Learning) for performing predictive maintenance analysis. In the supervised learning section, there is a complete guide to training, creating, and applying machine learning prediction models to predictive maintenance datasets.
- Stage 4: Operationalization teaches you how to apply the model to a broader implementation, and how to create reports and alerts for operational actions.


Installation guidelines : Precautionary instructions *** MUST READ ***
- Install the Splunk Essentials for Predictive Maintenance app on a test instance separate from production infrastructure. The app contains a sample dataset and therefore you might not want to install it on production infrastructure.

Prerequisite : Splunk apps required. *** MUST READ ***
- The following apps are required to use the “Splunk Essentials for Predictive Maintenance” app. Go to https://splunkbase.splunk.com to get apps that are required.

- Splunk Machine Learning Toolkit (4.1) : Splunk machine learning toolkit - https://splunkbase.splunk.com/app/2890/
- Python for Scientific Computing : Python environment for machine learning - https://splunkbase.splunk.com/app/2882/
- 3D Scatterplot - Custom Visualization : Visualization add-on for 3D scatter visualization - https://splunkbase.splunk.com/app/3138/ (Compatible with all 7.X versions or Splunk Enterprise, you must download and install)

WELCOME TO SPLUNK ESSENTIALS FOR PREDICTIVE MAINTENANCE

This Splunk Essential for Predictive Maintenance is created for maintenance reliability leaders and engineers who are looking for ways to improve and optimize their current preventive maintenance program. To do predictive maintenance right, you need to have the right amount of knowledge on how to process data and then apply machine learning techniques to achieve the desired results.

Designed for interactive learning, this guide focuses on learning how to solve real problems with real data. Users are provided with details of the predictive maintenance methodology and performance data from airplane engine sensors and then asked to operationalize their analytics.

The Splunk Essentials for Predictive Maintenance takes you through 4 stages of the analytics process. Each stage covers :
* Methodology - designed to cover essential analytics knowledge to practice predictive analytics.
* Step-by-step - designed to apply the knowledge to already included dataset.

   - Stage 1: Data collection and ingestion focuses on how you can easily use Splunk software to collect, store, and structure asset metrics (dataset from airplane jet engines included).
   - Stage 2: Data exploration covers key methods for pre-processing and exploring data to help you understand the type of data in use and the characteristics of the dataset, which is crucial for getting the desired outcome.
   - Stage 3: Analysis teaches you 3 key analysis options (Anomaly Detection, Unsupervised Learning, Supervised Learning) for performing predictive maintenance analysis. In the supervised learning section, there is a complete guide to training, creating, and applying machine learning prediction models to predictive maintenance datasets.
   - Stage 4: Operationalization teaches you how to apply the model to a broader implementation, and how to create reports and alerts for operational actions.

Installation guidelines : Precautionary instructions - - MUST READ **

Install the Splunk Essentials for Predictive Maintenance app on a test instance separate from production infrastructure. The app contains a sample dataset and therefore you might not want to install it on production infrastructure.

Prerequisite : Splunk apps required. - - MUST READ **

The following apps are required to use the “Splunk Essentials for Predictive Maintenance” app. Go to https://splunkbase.splunk.com to get apps that are required.

Release Notes

Version 0.1.9
March 16, 2019

Demo view fix.

Version 0.1.8
March 7, 2019

Minor Link Fix

Version 0.1.7
March 2, 2019

Demo search fix

Version 0.1.6
Feb. 27, 2019

Look up fix.

Version 0.1.5
Feb. 24, 2019

Font fixes

Version 0.1.4
Feb. 20, 2019

Fixed compatibility for boxplot in ML Toolkit 4.1

Version 0.1.3
Feb. 20, 2019

Fixes

Version 0.1.2
Feb. 20, 2019

Version 0.1.1
Feb. 18, 2019

FIX

Version 0.1.0
Feb. 18, 2019

Minor text fixes..

Version 0.0.9
Feb. 16, 2019

Version 0.0.8
Feb. 14, 2019

Fix minor description

Version 0.0.7
Feb. 14, 2019

Minor text fix

Version 0.0.6
Feb. 14, 2019

Fix default search index adding iot_pm index as search

Version 0.0.5
Feb. 14, 2019

Version 0.0.4
Feb. 12, 2019

Fixed minor text in the app.

Version 0.0.3
Feb. 12, 2019

Version 0.0.2
Feb. 11, 2019

Version 0.0.1
Jan. 30, 2019

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