A system for predictive modeling of wear or damage to a driven gear of an open gear set is provided. A method of developing a predictive model of wear or damage to a driven gear of an open gear set is also provided. The system and method allow for autonomous, non-interfering dynamic collection of data that are used to assist in developing maintenance schedules for large open gear sets. More specifically, it is directed to utilizing data from monitored pinion gears of girth gear sets under full load operating conditions to predict health of a girth gear in the girth gear set.

A system for predictive modeling of wear or damage to a driven gear of an open gear set is provided. A method of developing a predictive model of wear or damage to a driven gear of an open gear set is also provided. The system and method allow for autonomous, non-interfering dynamic collection of data that are used to assist in developing maintenance schedules for large open gear sets. More specifically, it is directed to utilizing data from monitored pinion gears of girth gear sets under full load operating conditions to predict health of a girth gear in the girth gear set.

A computer-implemented method for identifying a gearwheel (5) that induces vibrations in a transmission includes training a generic AI model with a training data set of a group of reference gearwheels (19). At least one reference profile of a surface (21) of the reference gearwheel (19) and a gearwheel category are provided for each reference gearwheel (19) of the group. The method also includes determining a profile of a surface (11) of a gearwheel (5) and assigning one of the gearwheel categories to the gearwheel (5) on the basis of the determined profile by the trained AI model.

A computer-implemented method for identifying a gearwheel (5) that induces vibrations in a transmission includes training a generic AI model with a training data set of a group of reference gearwheels (19). At least one reference profile of a surface (21) of the reference gearwheel (19) and a gearwheel category are provided for each reference gearwheel (19) of the group. The method also includes determining a profile of a surface (11) of a gearwheel (5) and assigning one of the gearwheel categories to the gearwheel (5) on the basis of the determined profile by the trained AI model.

A monitoring device for detecting a diagnostic state of an industrial machine on the basis of vibrations associated with the industrial machine, includes at least one measurement transducer for detecting at least one first signal characterizing the vibration of the industrial machine in a time period and for detecting at least one second signal characterizing the vibration of the industrial machine in the same time period, wherein the at least one measurement transducer has at least one signal processor for evaluating the at least first signal as a first characteristic value and for evaluating the at least second signal as a second characteristic value and wherein the at least one first characteristic value and the at least one second characteristic value form a characteristic value tuple, further having a storage unit, in which different diagnostic states describing the state of the industrial machine are saved, wherein the diagnostic states are determined as a function of the characteristic value tuple, and a predefined scalar range is saved with predetermined different fixed scalar values, which define respectively an existing limit potential, wherein a respective limit potential characterizes a change in the diagnostic state, and wherein between adjacent limit potentials a diagnostic partition is formed and wherein different scalar values from the scalar range are assigned to the different diagnostic partitions and wherein different respective diagnostic partitions represent different diagnostic states of the industrial machine, and wherein the monitoring device has at least one interpolation function for mapping the characteristic value tuple on a common, unique, time-dependent scalar value in the predefined scalar range, so that by way of the time-dependent scalar value a time monitoring of the diagnostic state of the industrial machine is achieved.

A method for automatic diagnosis of a mechanical system of a group of mechanical systems sharing mechanical characteristics includes obtaining data relating to a vibration. The vibration-related data is acquired by a portable communications device configured to communicate with a remote processor. The processor automatically diagnoses the mechanical system by applying a relationship to the obtained vibration-related data. The relationship is based on sets of vibration-related data previously obtained from the mechanical systems. Each set of vibration-related data relates to vibrations of a mechanical system. The relationship is further based on sets of operation data previously obtained for mechanical systems of the group. Each set of operation data indicates a previous state of operation of a mechanical system. Each of the previous states of operation is associated with at least one of the previously obtained sets of vibration-related data.

The present invention provides a monitoring method for a plate billet crank flying shear process, including: acquiring a control signal of a control system of a crank flying shear device, and determining a cutting stage of the crank flying shear process according to the control signal; obtaining an actual cutting edge speed curve in the cutting stage, and further dividing the cutting stage into multiple sub-processes according to the actual cutting edge speed curve; obtaining actual data of a parameter related to the crank flying shear process, and for one or more of the multiple sub-processes, separately comparing the actual data of the parameter with typical data of the parameter, in order to estimate an abnormality risk.

The present invention provides a monitoring method for a plate billet crank flying shear process, including: acquiring a control signal of a control system of a crank flying shear device, and determining a cutting stage of the crank flying shear process according to the control signal; obtaining an actual cutting edge speed curve in the cutting stage, and further dividing the cutting stage into multiple sub-processes according to the actual cutting edge speed curve; obtaining actual data of a parameter related to the crank flying shear process, and for one or more of the multiple sub-processes, separately comparing the actual data of the parameter with typical data of the parameter, in order to estimate an abnormality risk.

Methods and systems for in-situ determination of system response functions. One example method includes coupling a controlled blocked force exciter to a calibration receiver structure. The method also includes operating the controlled blocked force exciter under controlled operation conditions to induce vibration in the calibration receiver structure and measuring response data for the calibration receiver structure. The method further includes determining blocked forces based on the response data for the calibration receiver structure. The method also includes coupling the controlled blocked force exciter to a target receiver structure. The method further includes operating the controlled blocked force exciter under the controlled operation conditions to induce vibration in the target receiver structure and measuring response data for the target receiver structure. The method also includes determining system response functions by relating the response data for the target receiver structure to the blocked forces.

Methods and systems for in-situ determination of system response functions. One example method includes coupling a controlled blocked force exciter to a calibration receiver structure. The method also includes operating the controlled blocked force exciter under controlled operation conditions to induce vibration in the calibration receiver structure and measuring response data for the calibration receiver structure. The method further includes determining blocked forces based on the response data for the calibration receiver structure. The method also includes coupling the controlled blocked force exciter to a target receiver structure. The method further includes operating the controlled blocked force exciter under the controlled operation conditions to induce vibration in the target receiver structure and measuring response data for the target receiver structure. The method also includes determining system response functions by relating the response data for the target receiver structure to the blocked forces.