Design av testmiljö för verifiering av elektroniska styrenheter

Examensarbetet syftar till att undersöka möjligheten att expandera befintlig testprocess av elektroniska styrenheter. Hos Volvo Construction Equipment sker verifiering av styrenheter till stor del i riggar som består av större CAN-nätverk och innehåller många komponenter. Antalet riggar begränsas av att de är kostsamma, vilket i sin tur leder till att antalet tester som kan genomföras är begränsat. Målet med arbetet är att undersöka om det är möjligt att skapa en testmiljö som verifierar funktionalitet i en styrenhet, separerad från övriga delar av nätverket. Planen är att testmiljön ska kunna användas som komplement till de befintliga riggarna. Arbetet visar att det är möjligt genom att implementera en testmiljö som kan verifiera funktionalitet hos en separerad styrenhet. Testmiljön ger Volvo möjlighet att utföra fler tester och därmed expandera deras testprocess av elektroniska styrenheter.The thesis aims to examine the possibility of expanding the existing test process of Electronic Control Units. At Volvo Construction Equipment, verification of control units is mostly done in rigs that include large CAN-networks and contains multiple components. The number of rigs available is limited by their cost, which leads to a limited number of tests that can be made. The thesis is investigating whether it is possible to create a test environment that verifies functionality of an Electronic Control Unit, separated from the network. The purpose of the test environment is to be used as a complement to the existing rigs. The thesis shows that it is possible by implementing a test environment that can verify functionality of a separated control unit. This test environment allows Volvo to perform more tests and thereby expand their test process of Electronic Control Units

Virtual Sensing of Hauler Engine Sensors

The automotive industry is becoming more dependent on sustainable and efficient systems within vehicles. With the diverse combination of conditions affecting vehicle performance, such as environmental conditions and drivers' behaviour, the interest in monitoring machine health increases. This master thesis examines the machine learning approach to sensor reconstruction of hauler engine sensors for deviation detection applications across multiple domains. A novel proposal for sequence learning and modelling was by introducing a weighted difference of sequence derivatives. Impacts of including differences of derivatives assisted the learning capabilities of sequential data for the majority of the target sensors across multiple operating domains. Robust sensor reconstruction was also examined by using inductive transfer learning with a Long Short-Term Memory-Domain Adversarial Neural Network. Obtained results implied an improvement in using the Long Short-Term Memory-Domain Adversarial Neural Network, then using a regular Long Short-Term Memory network trained on both source and target domains. Suggested methods were evaluated towards model-based performance and computational limitations. The combined aspects of model performance and system performance are used to discuss the trade-offs using each proposed method

Virtual Sensing of Hauler Engine Sensors

The automotive industry is becoming more dependent on sustainable and efficient systems within vehicles. With the diverse combination of conditions affecting vehicle performance, such as environmental conditions and drivers' behaviour, the interest in monitoring machine health increases. This master thesis examines the machine learning approach to sensor reconstruction of hauler engine sensors for deviation detection applications across multiple domains. A novel proposal for sequence learning and modelling was by introducing a weighted difference of sequence derivatives. Impacts of including differences of derivatives assisted the learning capabilities of sequential data for the majority of the target sensors across multiple operating domains. Robust sensor reconstruction was also examined by using inductive transfer learning with a Long Short-Term Memory-Domain Adversarial Neural Network. Obtained results implied an improvement in using the Long Short-Term Memory-Domain Adversarial Neural Network, then using a regular Long Short-Term Memory network trained on both source and target domains. Suggested methods were evaluated towards model-based performance and computational limitations. The combined aspects of model performance and system performance are used to discuss the trade-offs using each proposed method