Failures and repairs – and the impacts of asset downtime – are issues that keep everyone awake at night. Things break. Parts wear out. Components fail. And when they do, it’s a scramble to fix them – and in the meantime, progress and productivity grind to a halt.
What it does
Predictive maintenance leverages data from an individual assets to predict failure. This way, repairs can be done when needed (and avoided when not). It offers the best upside, but at the cost of complexity
1) COLLECT: First, we need to gather relevant data that can help to predict time-to-failure, so we used IoT device simulator to create large fleet virtual connected devices and gather data using aws services.
2) PREDICT: The collected data is processed through connected vehicle solution and persist all vehicle health, trips, and vehicle owners in the dynamo DB.
3) REACT: Appian webAPI is invoked for each diagnostic code for the vehicle to Auto-schedule maintenance, send push notifications to warn staff of potential breakdowns and optimize your spare part inventory.
How I built it
AWS IoT Simulator:
IOT device simulation solution that enables customers to build a large fleet of virtual connected devices (widgets) from a user-defined template and simulate those widgets publishing data at regular intervals to AWS IoT.
AWS Connected Vehicle Solution:
This connected vehicle solution leverages the AWS IoT platform which authenticates messages from IOT Simulator and processes data according to five business rules. The solution’s stores vehicle data in DynamoDB tables that store various details about vehicle health, trips, and vehicle owners; a set of microservices (AWS Lambda functions) that process messages and data; an Amazon Kinesis Data Firehose delivery stream that encrypts and loads data to an Amazon Simple Storage Service (Amazon S3) bucket; an Amazon Kinesis Data Analytics application that analyzes data for anomalies; an Amazon Kinesis stream which enables real-time processing of anomalous data; and an Amazon Simple Notification Service (Amazon SNS) topic which sends alerts to users.
When AWS IoT receives a message, it authenticates and authorizes the message and the Rules Engine executes the appropriate rule on the message, which routes the message to the appropriate backend application.
Appian Auto-schedule maintenance, send push notifications to warn staff of potential breakdowns and optimize your spare part inventory.
Challenges I ran into
Learning Curve on Connected Vehicle Solution: we had limited support and there aren’t enough established QA capabilities, and only have a very small community to help in troubleshooting issues. This resulted in relatively long implementation cycle for connected vehicle solution.
Accomplishments that I'm proud of
Ability to expand Appian’s already vast number of real-life business related capabilities. Ability to expose multiple cutting edge technologies - IoT, machine learning libraries etc. all in a very short time window Full understand and appreciate the potential of Appian’s integration capabilities that include WebAPIs And finally, completing the project and successfully testing the integrations real-time and experiencing the power of Appian low code platform
What I learned
Appian is truly a great platform for business process automation. In our experience the current business users mostly focus on manually starting the business processes based on certain human-introduced events. With AWS connected vehicle integration, business processes don’t have to wait for the human intervention. Instead, your IoT devices are be “taught” to start the right business processes, involve the right set of stakeholders, and collect/display the most relevant information. The team now have a whole new spectrum of use cases where we can leverage different AI techniques and integrate them into the Appian Platform.
What's next for Predictive Maintenance
- Connected Car Alexa Skill
- Alexa skill to read values from many different cars
- Setup service appointment in Appian using Connect car Alexa skill