A lubricant delivery apparatus for installation on a device that includes a moving component that is actuated by an actuating fluid from an actuating fluid source is disclosed. The apparatus includes a lubricant supply reservoir and a pump operably coupled to the reservoir for receiving a pre-determined amount of lubricant from the reservoir and for discharging the pre-determined amount of lubricant from the apparatus. The apparatus has a non-actuated state, wherein the predetermined amount of lubricant is disposed within the pump and an actuated state wherein the predetermined amount of lubricant is discharged from the pump. While the apparatus is installed on the device, the pump is operably coupled to the actuating fluid source such that actuation of the moving component is with effect that the apparatus transitions from the non-actuated state to the actuated state such that the predetermined amount of lubricant is delivered to the moving component.

A machine learning apparatus (4) generates a learning model (6) to be used in a sliding-surface diagnosis apparatus (1A) for diagnosing a condition of sliding surfaces of a fixed-side sliding member and a rotation-side sliding member. The machine learning apparatus (4) includes: a learning-data memory (41) configured to store learning data including input data containing at least data on motor current value in a predetermined period, data on contact electric resistance in the predetermined period, and data on vibration (AE wave or acceleration) in the predetermined period; a machine learning section (42) configured to input the learning data to the learning model (6) to cause the learning model (6) to learn a correlation between the input data and diagnostic information of the sliding surfaces; and a learned-model memory (43) configured to store the learning model (6) that has learned by the machine learning section (42).

A machine learning apparatus (4) generates a learning model (6) to be used in a sliding-surface diagnosis apparatus (1A) for diagnosing a condition of sliding surfaces of a fixed-side sliding member and a rotation-side sliding member. The machine learning apparatus (4) includes: a learning-data memory (41) configured to store learning data including input data containing at least data on motor current value in a predetermined period, data on contact electric resistance in the predetermined period, and data on vibration (AE wave or acceleration) in the predetermined period; a machine learning section (42) configured to input the learning data to the learning model (6) to cause the learning model (6) to learn a correlation between the input data and diagnostic information of the sliding surfaces; and a learned-model memory (43) configured to store the learning model (6) that has learned by the machine learning section (42).

Detecting the occurrence of loss of effective lubrication in high-speed machinery components is provided. The imminent catastrophic failure may be predicted when torque or power transfer is lost. An estimate of when failure will likely occur throughout the operation of the machinery may be determined as well as the damage state after the liquid lubrication supply has ended or becomes inadequate to lubricate the machinery components effectively. By monitoring the concentration of gas species and the rate of change in concentration of the gas in the gearbox or machinery enclosure after the supply of the primary lubricant ends, determinations may be made about the time to failure and the damage state. The determinations may be based on thermomechanical and chemical processes, on measurement of a baseline system, or by setting a threshold of expected change in gas concentration. These determinations may be transmitted for further decision making and response.

Detecting the occurrence of loss of effective lubrication in high-speed machinery components is provided. The imminent catastrophic failure may be predicted when torque or power transfer is lost. An estimate of when failure will likely occur throughout the operation of the machinery may be determined as well as the damage state after the liquid lubrication supply has ended or becomes inadequate to lubricate the machinery components effectively. By monitoring the concentration of gas species and the rate of change in concentration of the gas in the gearbox or machinery enclosure after the supply of the primary lubricant ends, determinations may be made about the time to failure and the damage state. The determinations may be based on thermomechanical and chemical processes, on measurement of a baseline system, or by setting a threshold of expected change in gas concentration. These determinations may be transmitted for further decision making and response.

A working fluid monitoring system for monitoring a working fluid of working fluid system of a piece of equipment is provided. The working fluid monitoring system can include a filter member having an inlet, an outlet, and a filter media disposed between the inlet and the outlet. The filter member can be configured to permit fluid communication of the working fluid of the working fluid system from the inlet, through the filter media, and out the outlet of the filter member. A sensor is in operable communication with the working fluid within the filter member and is configured to monitor in situ a parameter of the working fluid and/or the working fluid system.

A working fluid monitoring system for monitoring a working fluid of working fluid system of a piece of equipment is provided. The working fluid monitoring system can include a filter member having an inlet, an outlet, and a filter media disposed between the inlet and the outlet. The filter member can be configured to permit fluid communication of the working fluid of the working fluid system from the inlet, through the filter media, and out the outlet of the filter member. A sensor is in operable communication with the working fluid within the filter member and is configured to monitor in situ a parameter of the working fluid and/or the working fluid system.

A diagnostic system for a lubrication circuit of an internal combustion engine of a vehicle. The system includes a viscometer for detecting the viscosity of a lubricating liquid of the lubrication circuit, a temperature sensor for detecting the temperature of the lubricating liquid, and a control unit to acquire the state of the lubricating liquid, given by the viscosity detected for a given lubricating liquid condition, which includes the lubricating liquid temperature and the date of last replacement of the lubricating liquid, and for a given condition of use of the engine, and to assess the state of the lubricating liquid by comparing the detected viscosity of the lubricating liquid with the viscosity reference values stored in the database in the same or similar condition of lubricating liquid temperature, date of last replacement of the lubricating liquid and use of the engine.

Systems and methods of monitoring lubrication of a gear assembly during operation of a machine provide for receiving at a control system, a signal from a sensor indicative of a value obtained by the sensor for an electrical property of a circuit crossing the gear assembly operably coupled to the machine, ascertaining whether the value for the electrical property corresponds to a warning level for a condition of the lubricant film, and outputting a control command when the value for the electrical property corresponds to the warning level for the condition of the lubricant film.

Systems and methods of monitoring lubrication of a gear assembly during operation of a machine provide for receiving at a control system, a signal from a sensor indicative of a value obtained by the sensor for an electrical property of a circuit crossing the gear assembly operably coupled to the machine, ascertaining whether the value for the electrical property corresponds to a warning level for a condition of the lubricant film, and outputting a control command when the value for the electrical property corresponds to the warning level for the condition of the lubricant film.