IOT Predictive Analytics

Abstract

1. Introduction

This IBM Pattern is intended for anyone who wants to experiment, learn, enhance and implement a new method for Predicting Equipment failure using IoT Sensor data. Sensors mounted on devices like IoT devices, Automated manufacturing like Robot arms, Process monitoring and Control equipment etc., collect and transmit data on a continuous basis which is Time stamped.

The first step would be to identify if there is any substantial shift in the performance of the system using Time series data generated by a single IoT sensor. For a detailed flow on this topic, you can refer to the Change Point detection IBM Pattern. Once, a Change point is detected in one key operating parameter of the IoT equipment, then it makes sense to follow it up with a Test to predict if this recent shift will result in a failure of an equipment. This Pattern is an end to end walk through of a Prediction methodology that utilizes multivariate IoT data to predict any failure of an equipment. Bivariate prediction algorithm – Logistic Regression is used to implement this Prediction. Predictive packages in Python 2.0 software is used in this Pattern with sample Sensor data loaded into the Data Science experience cloud.

All the intermediary steps are modularized and all code open sourced to enable developers to use / modify the modules / sub-modules as they see fit for their specific application

2. Objective

Method for Predicting failures in Equipment using Sensor data. Sensors mounted on devices like IoT devices, Automated manufacturing like Robot arms, Process monitoring and Control equipment etc., collect and transmit data on a continuous basis which is Time stamped.

This project demonstrates how Jupyter Notebook can be applied to a real-world Internet of Things (IoT) problem.

3. Key Features / Modules

• Analytics: Finding patterns in data to derive information.
• Data Science:Systems and scientific methods to analyze structured and unstructured data in order to extract knowledge and insights.

4. Technology Stack

• Reading IoT Sensor data from DB
• Function to split Test and Train datasets, Build Logistic Regression models, Score models, Compute accuracy metrics like Confusion matrix
• User configurable features and target variables for Predicting equipment failures, Test and Train data sets
• Computations of key statistics that help evaluate the Predictive capability of the models
• Repeat the experiment by altering the Configuration parameters by rerunning the models
• IBM Watson Studio: Analyze data using Python, Jupyter Notebook and RStudio in a configured, collaborative environment that includes IBM value-adds, such as managed Spark.
• DB2 Warehouse on cloud: IBM Db2 Warehouse on Cloud is a fully-managed, enterprise-class, cloud data warehouse service. Powered by IBM BLU Acceleration.
• IBM Cloud Object Storage: An IBM Cloud service that provides an unstructured cloud data store to build and deliver cost effective apps and services with high reliability and fast speed to market.

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/IBM/iot-predictive-analytics.git
cd iot-predictive-analytics

1. A blank, this indicates that the cell has never been executed.
2. A number, this number represents the relative order this code step was executed.
3. A , this indicates that the cell is currently executing.
4. One cell at a time.
5. Select the cell, and then press the Play button in the toolbar.
6. Batch mode, in sequential order.
7. From the Cell menu bar, there are several options available. For example, you
8. At a scheduled time.

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Add a mobile dashboard using Blynk or Firebase
• Store readings in a cloud database for history charts
• Add alerts via SMS / Telegram when thresholds are crossed

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. Which microcontroller / board and sensors are used and why?
2. How does the device send data (Wi-Fi, MQTT, HTTP, Bluetooth)?
3. Where is the sensor data stored and visualised?
4. How is power consumption managed?
5. How would you secure the device and its communication?

9. Source Code & License

This project is developed by IBM and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.