In Automotive Industries, to be confident regarding the success of a planned operation, performing accurate methods in order to detect abnormal operating conditions, known as faults, is crucial. An effective method for diagnosis and fault recognition ensures the safety of the operation, reduces manufacturing cost and any other potential impacts. In addition, mobile solutions have been widely adopted among automotive manufactures during recent years and they have taken full advantage of mobile strategies. Accordingly, it is necessary for there to be a future-proof plan to control the diagnostic operations in advance. In this thesis, the immediate objective has been to offer a future-proof and user-friendly solution to assist engineers and service technicians in the monitoring, detecting, and diagnosing of faults on Toyota/BT/CESAB branded trucks. A mobile cross-platform framework is used to develop the diagnostic mobile solution which is not only able to be deployed on Android and iOS mobile platforms, but also provides wireless communication between truck machines and mobile devices through Bluetooth and Wi-Fi ad hoc technologies. The diagnostic mobile tool is capable of processing real-time controller area network messages and visualizing the condition of different sensors in a more user-friendly way through rich hybrid and client-side web user interfaces. The experience of evaluating a cross-platform diagnostic tool on different mobile operating systems proved that cross-platform mobile development methodology can be a reliable technique for developing projects that essentially require real-time data processing. In addition, it indicates that Apple iOS offers a better runtime performance than Google Android for the current tool.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:miun-19830 |
| Date | January 2013 |
| Creators | Zamani, Ali |
| Publisher | Mittuniversitetet, Avdelningen för informations- och kommunikationssystem |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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