Shanghai Zhenhua Heavy Industry Co., Ltd. (ZPMC) is a leading heavy-duty equipment manufacturer, providing machinery for unloading and moving shipping containers once they arrive at port. ZPMC is in the process of transforming itself from being a traditional equipment manufacturer to becoming a comprehensive service provider in port planning, investing, construction, and operation.
Moving from on-premises to the cloud
The current port-machinery solution is on-premises and discrete. The programmable logic controllers (PLC) on machines such as quay cranes (QC), rail-mounted container gantry cranes (RMG), and automated guided vehicles (AGV) send thousands of signals every second to field OPC servers. The common way for ZPMC to monitor and retrieve device data is to log in to OPC servers remotely. ZPMC needs an easier and more elegant way to collect the device data and get alarm notifications.
To meet these needs, ZPMC requires an intelligent platform that can acquire real-time data from the machines that it is servicing all over the world and display that data in dashboards and interactive reports—plus generate global insights and enable predictive maintenance. ZPMC is also developing its next-generation automated port solution.
Developers at ZPMC turned to Microsoft to work with them on a machine-monitoring solution that takes advantage of Azure IoT Hub, Azure Stream Analytics, and Microsoft Power BI. This allowed them to centralize machinery data, deliver data insights in near-real-time reports and charts, and provide global monitoring capabilities.
Global machine monitoring in real time
The team defined two types of data messages to IoT Hub: alarm data and device status daily data. They designed the solution architecture using real-time data port machines, ingested by Azure IoT Hub, streamed to Stream Analytics, stored in Azure SQL Database and Blog storage, and monitored in Power BI.
Security consideration
Although the data transmission is on proprietary links from the machines to field OPC servers and then to the OPC agent, the data sent from the OPC agent to IoT Hub must travel over the Internet. In this project, the team chose the AMQP protocol to enhance the security of the device-to-cloud communication, which is natively supported by IoT Hub.
For the security of data storage in the cloud, they adopted some easy-to-configure security practices such as transparent data encryption for Azure SQL Database to help protect the digested alarms and statistics data.
Data visualization in Power BI
Power BI renders the real-time machine-status data from IoT Hub and the processed alarms from SQL Database and presents interactive reports to help teams gain insight and take action with their data.
Next Steps
Based on the current solution prototype, ZPMC would like to integrate more data from its ports and machines and build up a global remote monitoring center. The capability of maximizing the value of its device data and providing predictive maintenance service to its customers is on the top of ZPMC’s wish list. A proactive and predictive service—beyond traditional "report-response"—would impress its customers and provide a significant competitive advantage.
"IoT Hub is one of the key Azure services we used in the project. After we evaluated and used the IoT Hub service, we believe that it could remarkably ease our burdens to build a scalable, efficient, and easy-to-maintain data ingestion infrastructure. During the joint hackfest, the experts from Microsoft did a great job and helped us solve all the major issues from project architecture to data ingestion and to data visualization. Surely, this project would make a solid foundation to build up our predictive maintenance capability." —ZPMC
Get the code samples and architecture diagrams from this project on GitHub, watch the interview with the ZPMC and Microsoft team, get hands-on with Azure IoT Hub labs, or start developing a new solution with an Azure trial.
