Engineering Truck Predictive Maintenance Solution

Core device failure is fatal to enterprise manufacturing. Agricultural engineering trucks work in complex environments without stop. Faulty engineering trucks provoke immense losses, and high maintenance cost is also a large issue due to the global distribution of engineering trucks. In this case, manufacturers wish to reduce device risks, fault rate, and maintenance costs, and realize uninterrupted production, which, however, is hindered by the following challenges.

• Agricultural engineering trucks work in harsh environments such as farmlands, proposing high requirements for shockproof and dustproof data collection devices. Components of an engineering truck generate massive data with the maximum amount of 10 GBytes per day. In this case, data collection devices must be shockproof and dustproof, as well as support high bandwidth, large storage, and local data processing.

• Engineering trucks' status data such as GPS, speed, fuel consumption, fuel pressure, and temperature information needs to be uploaded in real-time, and other data such as worn component information needs to be transmitted to the cloud through carrier networks, causing high network transmission costs.

• The cloud system needs to process massive data, and discover problems and risks in real-time through analysis and modeling. However, high requirements are proposed for modeling, and the platform needs to remotely manage agricultural engineering trucks distributed around the globe, causing high deployment costs. Application systems also need open interfaces.

Predictive Maintenance Enables Efficient Production of Engineering Trucks

Solution Highlights

Provide innovative vehicle-mounted IoT gateways. The gateways use the industrial design, provide anti-electromagnetic interference and anti-vibration, and are dustproof and waterproof. They also support controller area network BUS (CANBUS) and LTE interfaces, and provide the data collection rate up to 250 kbit/s, meeting data collection requirements. In addition, the gateways support edge computing and storage. When running apps, these gateways collect engineering trunk status and component monitoring data through CANBUS interfaces, filter and process the collected data, upload real-time data to the cloud in a timely manner, and store non-real-time service data on local disks. Data processing policies can be delivered from the cloud, and changed based on service requirements.

Provide flexible data transmission policies. Vehicle status data including GPS, speed, fuel consumption, fuel pressure, and temperature information is backhauled to the cloud in real-time through carrier networks, meeting service requirements such as operation scheduling. Non-real-time data such as worn component information and network coverage faults are backhauled offline using Wi-Fi. In this case, data communication costs are effectively reduced.

Provide the cloud-based IoT platform to create data value and achieve predictive maintenance. The IoT platform is deployed on the DT public cloud, and provides IoT services to users through carrier networks. IoT gateways have the Agent software installed to connect to the platform, so that the platform can remotely deliver policies to ARs, upgrade software, and backhaul data. Open application programming interfaces (APIs) deliver data to application systems by connecting to application platforms. Leveraging manufacturers' abundant maintenance experience and data analysis based on Big Data, the solution realizes predictive maintenance, and reduces the truck fault rate.

Customer Value

Reduce the fault rate by 70%, fault time by 40%, and maintenance costs by 30%. When agricultural engineering trucks are faulty, the cloud analyzes data based on historical data to rapidly diagnose faults, which effectively shortens the fault recovery time.