A method (1000) of diagnosis of operation of a machine tool (10, 100) that includes one or more axes (X, Y, Z) moved by one or more actuators (101, 102, 104) and at least one sensor (30) coupled to the machine tool (10, 100), the method (1000) comprising operations of: generating (1200) a programming sequence of movement of the axes (X, Y, Z) of the machine tool (10, 100); controlling (1210) the movement of the axes (X, Y, Z) of the machine tool (10, 100) according to the programming sequence; receiving (1220) a read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100); and processing (1230) the read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100). The programming sequence comprises instructions that are such as to apply (T) at least one single impulsive variation of a kinematic quantity that regards one or more actuators (101, 102, 104). The operation (1230) of processing the read-out signal (S) comprises processing a response of the machine tool (10, 100) to at least one single impulsive variation. The operation (1230) of processing the read-out signal (S) comprises artificial-neural-network processing (206) via one or more artificial neural networks (206, 2060) configured for analysing operating profiles in particular, one or more signals indicative of the status of the machine tool (W) in the read-out signal (S).

A distress detection system includes a data repository operable to collect sensor data from a monitored system. The distress detection system also includes an analysis system with a processing system operable to access a first parameter of a first system of the monitored system from the data repository and access a second parameter of the first system of the monitored system from the data repository. The processing system is also operable to apply fuzzy reasoning rules to evaluate a combination of the first parameter and the second parameter to determine an in-range interaction with respect to a second system of the monitored system as fuzzy metric data points, classify a component of the second system as being in distress based on comparing the fuzzy metric data points to a limit line, and assert a component distress indicator responsive to classifying the component of the second system as being in distress.

A distress detection system includes a data repository operable to collect sensor data from a monitored system. The distress detection system also includes an analysis system with a processing system operable to access a first parameter of a first system of the monitored system from the data repository and access a second parameter of the first system of the monitored system from the data repository. The processing system is also operable to apply fuzzy reasoning rules to evaluate a combination of the first parameter and the second parameter to determine an in-range interaction with respect to a second system of the monitored system as fuzzy metric data points, classify a component of the second system as being in distress based on comparing the fuzzy metric data points to a limit line, and assert a component distress indicator responsive to classifying the component of the second system as being in distress.

A method (1000) of diagnosis of operation of a machine tool (10, 100) that includes one or more axes (X, Y, Z) moved by one or more actuators (101, 102, 104) and at least one sensor (30) coupled to the machine tool (10, 100), the method (1000) comprising operations of: generating (1200) a programming sequence of movement of the axes (X, Y, Z) of the machine tool (10, 100); controlling (1210) the movement of the axes (X, Y, Z) of the machine tool (10, 100) according to the programming sequence; receiving (1220) a read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100); and processing (1230) the read-out signal (S) of the at least one sensor (30) coupled to the machine tool (10, 100). The programming sequence comprises instructions that are such as to apply (T) at least one single impulsive variation of a kinematic quantity that regards one or more actuators (101, 102, 104). The operation (1230) of processing the read-out signal (S) comprises processing a response of the machine tool (10, 100) to at least one single impulsive variation. The operation (1230) of processing the read-out signal (S) comprises artificial-neural-network processing (206) via one or more artificial neural networks (206, 2060) configured for analysing operating profiles in particular, one or more signals indicative of the status of the machine tool (W) in the read-out signal (S).