Explain the features of Amazon Monitron for Redis

In this recipe, we will learn about Amazon Monitron for Redis. We will also learn about the features of Amazon Monitron for Redis.

Recipe Objective - Explain the features of Amazon Monitron for Redis?

The Amazon Monitron is a widely used service. It is defined as a fully managed service which provides Predictive maintenance and machine learning that can help users avoid unplanned equipment downtime. Machine learning (ML) can detect and respond to machine issues before they occur. With Amazon Monitron's end-to-end system, users can start monitoring equipment in minutes with simple installation and automatic, secure analysis. As Amazon Monitron learns from technician feedback entered in the mobile and web apps, system accuracy improves over time. Instead of going after "everything," AWS Monitron cleverly targeted industries to track and monitor a wide range of rotating machinery. Motors, gearboxes, pumps, fans, bearings, and compressors are just a few examples. Rotating equipment is used in every industry in some way. The idea is to track the vibrations and temperature of rotating machinery rather than anything else that would require specialised sensors to monitor equipment failure. The goal is to use the same sensor for all rotating machinery, which should cover the majority of the machinery. The strategy is to get these industries to jumpstart innovation and see results quickly. If you fail, it will assist you in failing quickly. Use a sensor that is "standard" and can be mounted on rotating equipment. These sensors must support OTA (Over-The-Air) firmware updates to be future-proof. Use BlueTooth (BLE) for connectivity because it is standard, low-energy, and long-lasting. Create a user-friendly IoT platform using AWS IoT Services (such as Lambda, S3, DynamoDB, and others). Good documentation to help you get up and running quickly and reap the benefits. The goal is to keep the usability so basic that no high-level resources are required to get started. This helps users save both time and money.

ETL Orchestration on AWS using Glue and Step Functions

Benefits of Amazon Monitron

  • Use the built-in Machine Learning (ML) feature in Amazon Monitron, which analyses usage patterns based on vibrations and provides a predictive inference. This is an excellent step because it eliminates the majority of the technological grey areas where industries must experiment. This is a time and money saver. Over time, using machine learning will improve accuracy. A mobile app that can be used to keep track of the equipment. When Monitron detects potential failures, users can receive push notifications and view sensor measurements inside the app. The wireless sensors from Amazon Monitron can be easily adhered to users' equipment with adhesive, eliminating the need for costly and inconvenient cabling. These low-cost sensors are designed to record vibration and temperature data to keep track of the health of your rotating machinery. Wi-Fi and ethernet gateways are also included in Amazon Monitron to transfer sensor data to AWS. Sensors and gateways from Amazon Monitron are pre-configured to work with the Amazon Monitron service and thus provide wireless sensor and gateway system from start to finish.

System Requirements

  • Any Operating System(Mac, Windows, Linux)

This recipe explains Amazon Monitron and its Features of Amazon Monitron.

Features of Amazon Monitron

    • It provides feedback on alert

Amazon Monitron for Redis provides users with the opportunity to enter feedback on the alerts received, such as failure mode, failure cause, and action is taken, with just a few taps in the mobile and web apps. Amazon Monitron improves over time as a result of the feedback it receives.

    • It provides timely notifications in the Amazon Monitron app

When Amazon Monitron detects abnormal machine patterns based on vibration and temperature settings, it sends push notifications. Within the app, you can also review and track these abnormal machine states.

    • It provides ISO and ML-based analytics

Amazon Monitron analyses vibration and temperature signals using ISO 20816 standards for vibration and ML-enabled models to detect abnormal machine operating states.

    • It provides a simple device set-up with the Amazon Monitron Mobile App

By tapping users' phone on the sensor and using near-field communication (NFC) technology, users can quickly and easily set up Amazon Monitron Sensors. Set up your Gateway in the app by following a few simple steps. Without any development work, users can quickly install and start using these devices to monitor their equipment.

    • It finds its use case in Gearboxes

Amazon Monitron provides easy-to-install hardware and with the power of machine learning, users can avoid costly repairs and factory equipment downtime in the Gearboxes.

What Users are saying..

profile image

Anand Kumpatla

Sr Data Scientist @ Doubleslash Software Solutions Pvt Ltd
linkedin profile url

ProjectPro is a unique platform and helps many people in the industry to solve real-life problems with a step-by-step walkthrough of projects. A platform with some fantastic resources to gain... Read More

Relevant Projects

Getting Started with Pyspark on AWS EMR and Athena
In this AWS Big Data Project, you will learn to perform Spark Transformations using a real-time currency ticker API and load the processed data to Athena using Glue Crawler.

Deploy an Application to Kubernetes in Google Cloud using GKE
In this Kubernetes Big Data Project, you will automate and deploy an application using Docker, Google Kubernetes Engine (GKE), and Google Cloud Functions.

Analyse Yelp Dataset with Spark & Parquet Format on Azure Databricks
In this Databricks Azure project, you will use Spark & Parquet file formats to analyse the Yelp reviews dataset. As part of this you will deploy Azure data factory, data pipelines and visualise the analysis.

Graph Database Modelling using AWS Neptune and Gremlin
In this data analytics project, you will use AWS Neptune graph database and Gremlin query language to analyse various performance metrics of flights.

AWS Project - Build an ETL Data Pipeline on AWS EMR Cluster
Build a fully working scalable, reliable and secure AWS EMR complex data pipeline from scratch that provides support for all data stages from data collection to data analysis and visualization.

Real-Time Streaming of Twitter Sentiments AWS EC2 NiFi
Learn to perform 1) Twitter Sentiment Analysis using Spark Streaming, NiFi and Kafka, and 2) Build an Interactive Data Visualization for the analysis using Python Plotly.

Azure Data Factory and Databricks End-to-End Project
Azure Data Factory and Databricks End-to-End Project to implement analytics on trip transaction data using Azure Services such as Data Factory, ADLS Gen2, and Databricks, with a focus on data transformation and pipeline resiliency.

Python and MongoDB Project for Beginners with Source Code-Part 1
In this Python and MongoDB Project, you learn to do data analysis using PyMongo on MongoDB Atlas Cluster.

Learn Data Processing with Spark SQL using Scala on AWS
In this AWS Spark SQL project, you will analyze the Movies and Ratings Dataset using RDD and Spark SQL to get hands-on experience on the fundamentals of Scala programming language.

Movielens Dataset Analysis on Azure
Build a movie recommender system on Azure using Spark SQL to analyse the movielens dataset . Deploy Azure data factory, data pipelines and visualise the analysis.