A system comprises a plurality of handheld devices including a plurality of transceivers. A control unit may be coupled to a handheld device of the plurality of handheld devices. The system may further include a plurality of end-of-train air devices coupled to a plurality of air brakes. An air manifold may be coupled to the plurality of air brakes. The system may include a controller coupled to the control unit and to the air manifold. A processor may be coupled to the controller and the control unit, and a non-transitory computer readable medium may be coupled to the processor. The non-transitory computer readable medium may include instructions executable to receive information from the control unit corresponding to an air brake test performed on the plurality of end-of-train air devices, determine a status of the air brake test, and generate an inspection form based on the received information and the determined status.

A vehicle readiness system includes a processing circuit. The processing circuit is configured to receive subsystem diagnostic data regarding a status of a subsystem of a vehicle, receive inventory data regarding an inventory of equipment present on the vehicle, receive inspection data regarding a condition of an inspection point on the vehicle, determine a readiness score for the vehicle based on the subsystem diagnostic data, the inventory data, and the inspection data, and transmit a readiness report based on the readiness score, the subsystem diagnostic data, the inventory data, and the inspection data to at least one of a display device of the vehicle or a user device separate from the vehicle.

Systems, methods, and apparatuses are provided for evaluating the calibration requirements or calibration needs of one or more sensors of a vehicle. A subject matter expert and/or a machine learning model can be used to generate correlations between data scanned from a vehicle and from repair orders or repair estimates. Natural language processing can be used to evaluate information contained in a repair order to generate a CEICA or line code. The machine learning model can use rules when a diagnostic trouble code (DTC) provides a high probability indication that a particular component requires repair. In some examples, a machine learning model can cluster or otherwise identify a likely area of repair based on information embedded or contained in a repair estimate.

A sensing system for a vehicle includes wheel end sensors (WES), each WES proximate to at least one predetermined tire and configured to sense angular data, including at least one of angular position, angular velocity, angular acceleration, and/or angular displacement. The system includes a plurality of tire pressure monitoring (TPM) sensors, each TPM sensor inside one of the tires and configured to sense pressure, temperature, and angular data within said tire. The sensing system is configured to compare the angular data sensed by each WES to the angular data sensed by each TPM sensor to automatically identify, for each TPM sensor, a corresponding WES, and based on the position on the vehicle of the predetermined tire of the corresponding WES, identify the position on the vehicle corresponding to said TPM sensor.

Aspects of the present invention relate to a vehicle controller comprising at least two virtual machines being hosted on the vehicle controller. The vehicle controller comprises an input for receiving a diagnostic command from a diagnostic tool. Furthermore, the controller comprises a first virtual machine hosted on the vehicle controller comprising a diagnostic module and a second virtual machine hosted on the vehicle controller comprising a vehicle service comprising diagnostic data. The diagnostic module is configured to initiate a diagnostic function on the vehicle service upon receipt of a diagnostic command at the input. The controller further comprises an output configured to output a diagnostic signal indicative of a result of the diagnostic function to the diagnostic tool.

A system comprises a dongle and a diagnostic tool. The dongle includes a vehicle communication transceiver (VCT), a first wireless transceiver, and a vehicle connector. The diagnostic tool includes a processor, a proximity sensing component, an output device, and a second wireless transceiver. The proximity sensing component outputs an output signal. The processor receives the output signal and make a determination that indicates whether the output signal indicates an object is in spatial proximity to the proximity sensing component. The processor outputs using the output device a notification based on the determination. The VCT performs a vehicle communication directly with a vehicle while the vehicle connector is connected to an on-board diagnostic connector (OBDC) of the vehicle. The first and second wireless transceivers communicates with each other to trigger a vehicle communication from the dongle to the vehicle while the vehicle connector is connected to the OBDC.

A method for performing vehicle remote diagnosis is provided. A diagnostic device sends a first controller area network (CAN) bus message to a device connector. The device connector encapsulates the first CAN bus message received into a first data packet and sends the first data packet to a vehicle connector through remote communication. The vehicle connector decapsulates the first data packet into the first CAN bus message and sends the first CAN bus message to a target vehicle. The vehicle connector receives second CAN bus messages and filters the second CAN bus messages to obtain a CAN bus diagnostic message, and the second CAN bus messages are sent by the target vehicle in response to the first CAN bus message. The vehicle connector encapsulates the CAN bus diagnostic message into a second data packet and sends the second data packet to the device connector.

A filter element analysis system for analyzing a filter element within a vehicle, the system including various filter sensors so as to provide information regarding various filter element parameters, a locator which configured provide vehicle position information such that conditions regarding the vehicle environment can be tracked and correlated to the location, as well as a means for transmitting information to a remote server for analysis and tracking of the filter element information with regard to environmental conditions such that a filter element status, remaining filter life, or particle load and replacement timeline can be calculated and updated so as to provide more accurate predictive models of the filter element conditions. As well as provide alerts regarding the need and scheduling of replacement or cleaning of a particular filter element.

An example method involves receiving, at a computing system, vehicle diagnostic information from a vehicle. The vehicle diagnostic information may include one or more sets of parameters corresponding to parameter identifiers (PIDs). The method further involves identifying a first set of parameters corresponding to a PID representing a state of a particular system of the vehicle and determining, using the first set of parameters, a current value of the PID. The method may also involve performing a comparison between the current value and a predetermined value for the PID and determining a health of the particular system of the vehicle such that the health reflects a difference between the current value and the predetermined value for the PID. The method may also involve displaying, by the computing system at a graphical interface, a vehicle health record representing the health of the particular system of the vehicle.

A method includes determining a test drive script (TDS) to perform while a tool monitors an electronic system in a mobile machine during a test drive of the mobile machine. The TDS includes an ordered sequence of drive cycle procedures (DCPs). The ordered sequence of DCPs begins with an initial DCP and ends with a final DCP. Each DCP is indicative of a respective mobile machine state. The TDS includes a first particular DCP associated with both a first particular mobile machine state and a first condition pertaining to the first particular mobile machine state. The method also includes outputting a representation of at least a portion of the TDS. Additionally, the method includes determining and outputting one or more of: status information corresponding to achieving the first particular mobile machine state or status information corresponding to achieving the first condition pertaining to the first particular mobile machine state.