A machine tool includes: a movable body that supports a tool; a feed drive unit that feed-drives the movable body; a numerical control unit that controls the feed drive by the feed drive unit to perform cutting by the tool; an acceleration sensor that is provided on the movable body and detects acceleration of the movable body; and a command unit that outputs a stop command to stop driving of the feed drive unit to the numerical control unit when the acceleration of the movable body exceeds a threshold value set in advance on the basis of an output of the acceleration sensor. The command unit sets the threshold value to a first threshold value in a cutting control state, and sets the threshold value to a second threshold value lower than the first threshold value in a feed control in a non-cutting control state.

A collection device includes a state correlation information storage unit that stores state correlation information indicating correlative relationships between control information relating to control of an industrial machine and environment information relating to the environment of the industrial machine, a control information acquisition unit that acquires control information from the industrial machine, an environment information acquisition unit that acquires environment information relating to the environment of the industrial machine, a state inference unit that infers the current state of the industrial machine on the basis of the control information, the environment information, and the state correlation information, and a proxy response unit that responds to an inquiry from another device on behalf of the industrial machine on the basis of the inferred state of the industrial machine.

A collection device includes a state correlation information storage unit that stores state correlation information indicating correlative relationships between control information relating to control of an industrial machine and environment information relating to the environment of the industrial machine, a control information acquisition unit that acquires control information from the industrial machine, an environment information acquisition unit that acquires environment information relating to the environment of the industrial machine, a state inference unit that infers the current state of the industrial machine on the basis of the control information, the environment information, and the state correlation information, and a proxy response unit that responds to an inquiry from another device on behalf of the industrial machine on the basis of the inferred state of the industrial machine.

A work machine includes a chassis, a wheel, an implement, a user interface, and a utilization monitoring system. The wheel is rotatably coupled to the chassis. The implement is movable relative to the chassis. The user interface is configured to receive a user input. The utilization monitoring system includes one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to obtain one or more values representing an operational range of the implement; receive the user input; determine a value representing a position of the implement; and determine a value representing a utilization of the implement by comparing the position of the implement to the one or more values representing the operational range of the implement.

A work machine includes a chassis, a wheel, an implement, a user interface, and a utilization monitoring system. The wheel is rotatably coupled to the chassis. The implement is movable relative to the chassis. The user interface is configured to receive a user input. The utilization monitoring system includes one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to obtain one or more values representing an operational range of the implement; receive the user input; determine a value representing a position of the implement; and determine a value representing a utilization of the implement by comparing the position of the implement to the one or more values representing the operational range of the implement.

Methods are provided for accurately blending biodiesel into distillate streams to achieve a pre-determined percentage of biodiesel in the distillate, applicable to wild-type distillate streams as well as distillate streams that already contain some percentage of biodiesel.

Methods are provided for accurately blending biodiesel into distillate streams to achieve a pre-determined percentage of biodiesel in the distillate, applicable to wild-type distillate streams as well as distillate streams that already contain some percentage of biodiesel.

A method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation. In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.

A method according to the disclosure configures a processor to predict metal loss in a structure for remediation. The method uses a machine learning model, trained based upon historical data, to predict metal loss over locations of a structure at a time of the prediction. The method identifies from among the predicted locations a high-risk location on the structure in which a magnitude of metal loss indicates potential remediation being needed, dispatches a robotic vehicle to the high-risk location on the structure and inspects the high-risk location using the robotic vehicle to confirm whether the magnitude of metal loss at the location requires remediation. In further methods, remediation is performed. In still further methods, a three-dimensional visualization of the structure is generated with an overlay which depicts predicted metal loss over the sections of the structure.

A system for controlling an operation of a machine including a plurality of actuators assisting one or multiple tools to perform one or multiple tasks, in response to receiving an acoustic mixture of signals generated by the tool performing a task and by the plurality of actuators actuating the tool, submit the acoustic mixture of signals into a neural network trained to separate from the acoustic mixture a signal generated by the tool performing the task from signals generated by the actuators actuating the tool to extract the signal generated by the tool performing the task from the acoustic mixture of signals, analyze the extracted signal to produce a state of performance of the task, and execute a control action selected according to the state of performance of the task.