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

Jingwei Li

Graduate Research assistance at Stony Brook University
linkedin profile url

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data.... Read More

Relevant Projects

Build an ETL Pipeline with DBT, Snowflake and Airflow
Data Engineering Project to Build an ETL pipeline using technologies like dbt, Snowflake, and Airflow, ensuring seamless data extraction, transformation, and loading, with efficient monitoring through Slack and email notifications via SNS

A Hands-On Approach to Learn Apache Spark using Scala
Get Started with Apache Spark using Scala for Big Data Analysis

Implementing Slow Changing Dimensions in a Data Warehouse using Hive and Spark
Hive Project- Understand the various types of SCDs and implement these slowly changing dimesnsion in Hadoop Hive and Spark.

PySpark Project-Build a Data Pipeline using Kafka and Redshift
In this PySpark ETL Project, you will learn to build a data pipeline and perform ETL operations by integrating PySpark with Apache Kafka and AWS Redshift

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.

Build a Data Pipeline in AWS using NiFi, Spark, and ELK Stack
In this AWS Project, you will learn how to build a data pipeline Apache NiFi, Apache Spark, AWS S3, Amazon EMR cluster, Amazon OpenSearch, Logstash and Kibana.

SQL Project for Data Analysis using Oracle Database-Part 2
In this SQL Project for Data Analysis, you will learn to efficiently analyse data using JOINS and various other operations accessible through SQL in Oracle Database.

Getting Started with Azure Purview for Data Governance
In this Microsoft Azure Purview Project, you will learn how to consume the ingested data and perform analysis to find insights.

SQL Project for Data Analysis using Oracle Database-Part 7
In this SQL project, you will learn to perform various data wrangling activities on an ecommerce database.

AWS Project for Batch Processing with PySpark on AWS EMR
In this AWS Project, you will learn how to perform batch processing on Wikipedia data with PySpark on AWS EMR.