The present invention provides a lawn mower comprising a battery system, a walking system and a cutter system. The lawn mower further comprises: a fault detection module, configured to detect if at least one of the battery system, walking system and cutter system has a fault; a fault determination module, configured to judge the fault level of the detected fault; and a control module, to control the current-limiting or outage of the corresponding battery system, the slowdown or stop of the walking system, as well as the slowdown or stop of the cutter system according to the fault level; compared with the prior art, the invention not only makes the fault detection more intelligent, but also the lawn mower can adjust the working state in real time according to the fault level, thereby improving the working efficiency.

An embodiment wheel base includes a first base including a first wheel, a second base including a second wheel and coupled to the first base, a locking unit including a driving part and a fixing part operated by the driving part and selectively coupled to a plurality of fixing reception parts in the second base for coupling between the first and second bases, and a processor electrically connected to the locking unit, wherein, in response to receiving a base length adjustment command, the processor initializes a position value of the driving part acquired by a position value acquisition part, drives the driving part to achieve an unlocked state, and in response to an absolute value of the position value of the driving part acquired after the driving part is driven being less than or equal to a first reference position value, determines that an unlocking failure has occurred.

A method for analyzing a sensor with respect to unstable errors, wherein a gradient signal is generated based on a sensor signal. Based on the gradient signal, an evaluation unit is used to assign a slope category to a first slope of the sensor signal and a second slope of the sensor signal, the second slope following the first slope. The evaluation unit ascertains a class of error based on the slope categories assigned to the first slope and the second slope.

A method including sending a challenge from a vehicle to a remote driving station, wherein the challenge causes an input device control unit to apply at least one output signal to a circuitry; receiving in the vehicle a response from the remote driving station, wherein the response indicates at least one input signal received by the input device control unit from the circuitry in response to the applied at least one output signal; and determining, by a vehicle control unit of the vehicle, at least one of the state of a manually operable input device and whether the circuitry is faulty based on the sent challenge and the received response.

A method is provided for repair of a mechanical or electromechanical system of a machine. An inference engine receives indication of a failure mode of the system, and measurements of operating conditions of the machine, and the inference engine defines a current problem including (a) the failure mode of the system, and (b) a pattern in the measurements. The inference engine searches a knowledge base with historical problems including (a) failure modes of systems of the machine, and (b) patterns in measurements of the operating conditions, and respective solutions with (c) repair actions performed to address the respective ones of the failure modes, for a respective solution to a historical problem most similar to the current problem. This is inferred as a solution to the current problem, and the inference engine generates an output display indicating the repair action of the solution to address the failure mode of the system.

An asset protection, monitoring, or control device is operative to execute a decision-making logic to process inputs and generate a decision-making logic output that comprises one or more time series, process the decision-making logic output using a machine learning model, and cause an action to be performed responsive to a machine learning model output.