IoT Edge Analytics: Perform analysis with IoT elements

Equipment Failure Prediction using IoT Sensor data

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.

Emerging Tech

IoT Equipment failure prediction involves collecting Sensor values and running Predictive algorithms to predict any impending failure. Core building blocks would include identifying the features or factors contributing to equipment failures. Then configuring a Predictive model to Train the model followed by Scoring the Test data for checking the reliability of the Predictive model. Python 2.0 software is used in this Journey with sample Sensor data loaded into the Data Science experience cloud.

By Krishna Prabu D

• https://github.com/IBM/iot-predictive-analytics

• https://youtu.be/k8uPYd3jUFs

This developer journey 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 Developer Journey.

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 journey 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 Journey 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 When you have completed this journey, you will understand how to

1. Read IoT Sensor data stored in the Data base
2. Configure the features and target variables for Prediction model
3. Split the multivariate data into Train and Test datasets by configuring the ratio
4. Train the model using Logistic Regression and measure the Prediction accuracy
5. Score the Test data and measure Prediction accuracy
6. Evaluate the Model’s Predictive performance further by computing a Confusion matrix
7. Rerun experiments by changing the configuration parameters

1. User signs up for IBM Data Science experience
2. User loads the sample IoT sensor Time series data to database
3. A configuration file holds all the key parameters for running the IoT Time series prediction algorithm
4. The prediction algorithm written in Python 2.0 Jupyter notebook uses the Configuration parameters and Sensor data from DB
5. Python Notebook runs on Spark in IBM DSX to ensure performance and scalability
6. The outputs of the prediction algorithm is saved in Object storage for consumption

• IBM Data Science Experience: Analyze data using RStudio, Jupyter, and Python 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.
• Bluemix Object Storage: A Bluemix 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.

• Python 2.0: Python is a free software environment that has many applications in the field of Web and Internet development, Scientific and Numeric computing, Desktop GUIs, Software Development, Business Applications. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
• Jupyter Notebooks: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text.
• Data Science: Systems and scientific methods to analyze structured and unstructured data in order to extract knowledge and insights.
• Analytics: Analytics delivers the value of data for the enterprise.

IoT Predictive Analytics is used to Predict any impending Equipment failure using Predictive algorithms that leverage multivariate data collected from several IoT sensors. The initial step would be to detect if there are any abrupt change in the Equipment behavior which can be done using IoT Time series data. Refer to IoT Change Point detection Developer journey for more details on this. Once the symptoms of anomaly is confirmed using a Change point detection methods, the logical next step would be to predict the possibility of any equipment failure in the near future. This prediction step is the focus for this journey. Predictive algorithms implemented in Python 2.0 on IBM Data Science Experience platform is used with data stored in IBM Bluemix database to implement this journey.

All components are open sourced and designed as modules to enable reuse of components either individually or as a whole. The entire flow is made configurable so that multiple iterations of the Predictive model can be run by merely re-configuring parameters and initiating a re-run of the flow.

• Internet of Things And Predictive Maintenance Transform The Service Industry: The field service industry is huge, encompassing 20 million field technicians in vans spread across the world, maintaining everything from hospital equipment to office elevators, heavy manufacturing machines and wind farm turbines, and there are a lot of efficiencies which can be made.

• Predict Equipment Failure With Advanced Analytics: Asset-intensive organizations are investing in advanced analytics to help them predict the failure of mission-critical equipment or assets.

• Predictive Analytics to improve Equipment maintenance: Maintenance is generally classified into three classes: corrective maintenance (CM), preventive maintenance (PM), condition based maintenance (CBM) which more recently has been termed predictive maintenance (PdM).

• Logistic Regression: In statistics, logistic regression, or logit regression, or logit model[1] is a regression model where the dependent variable (DV) is categorical. This article covers the case of a binary dependent variable—that is, where the output can take only two values, "0" and "1", which represent outcomes such as pass/fail, win/lose, alive/dead or healthy/sick.

• A Survey of Methods for Time Series Change Point Detection: Change points are abrupt variations in time series data. Such abrupt changes may represent transitions that occur between states. Detection of change points is useful in modelling and prediction of time series and is found in application areas such as medical condition monitoring, climate change detection, speech and image analysis, and human activity analysis. This survey article enumerates, categorizes, and compares many of the methods that have been proposed to detect change points in time series.