A method is for generating a knowledge base useful in identifying and/or predicting a malfunction of a medical device. The method includes (a) providing virtual components, each corresponding to a component of the medical device; (b) creating a virtual model of the medical device using the virtual components; (c) rendering at least one virtual output parameter of the virtual model, based upon simulation of a malfunction of at least one of the virtual components; (d) providing the at least one virtual output parameter rendered, to the medical device software; (e) correlating a response of the medical device software to the at least one virtual output parameter rendered; steps (c) to (e) being repeated, based upon a plurality of different simulated malfunctions, and the plurality of different simulated malfunctions and responses of the medical device software correlated being used in the generating of the knowledge base on the medical device.

A method is for generating a knowledge base useful in identifying and/or predicting a malfunction of a medical device. The method includes (a) providing virtual components, each corresponding to a component of the medical device; (b) creating a virtual model of the medical device using the virtual components; (c) rendering at least one virtual output parameter of the virtual model, based upon simulation of a malfunction of at least one of the virtual components; (d) providing the at least one virtual output parameter rendered, to the medical device software; (e) correlating a response of the medical device software to the at least one virtual output parameter rendered; steps (c) to (e) being repeated, based upon a plurality of different simulated malfunctions, and the plurality of different simulated malfunctions and responses of the medical device software correlated being used in the generating of the knowledge base on the medical device.

A method for estimating a Remaining Useful Life of a subject equipment, with a preliminary phase including the following steps: acquire test observations (step 10) and produce test time series (S.sub.x) of at least one signature; partition the test time series to obtain severity classes corresponding to the ageing phases of the test equipment devices (step 14); carry out an initial learning of a diagnosis model on the test equipment devices (step 45); perform a second learning of a signature prediction model (step 51). There is also an operational phase including the following steps: acquire observations when in operation on the subject equipment and produce an extrapolated time series using the prediction model; classify the extrapolated time series using the diagnosis model and derive the remaining useful life of the subject equipment.

A method for estimating a Remaining Useful Life of a subject equipment, with a preliminary phase including the following steps: acquire test observations (step 10) and produce test time series (S.sub.x) of at least one signature; partition the test time series to obtain severity classes corresponding to the ageing phases of the test equipment devices (step 14); carry out an initial learning of a diagnosis model on the test equipment devices (step 45); perform a second learning of a signature prediction model (step 51). There is also an operational phase including the following steps: acquire observations when in operation on the subject equipment and produce an extrapolated time series using the prediction model; classify the extrapolated time series using the diagnosis model and derive the remaining useful life of the subject equipment.

An operation system of a working machine mounted on the working machine, including a first control device to control the working machine, and a first command device to output a command to the first control device wirelessly, the command relating to control of the first control device. The first command device includes a first obtaining part to obtain an expiration date of the working machine from outside, a first time calculating part to obtain time, a third processing part to judge whether the time obtained by the first time calculating part meets the expiration date, and a sixth communication part to output a first command when the time meets the expiration date, the first command restricting control of the first control device, and to output a second command when the time does not meet the expiration date, the second command allowing the first control device to be controlled without restriction.

An operation system of a working machine mounted on the working machine, including a first control device to control the working machine, and a first command device to output a command to the first control device wirelessly, the command relating to control of the first control device. The first command device includes a first obtaining part to obtain an expiration date of the working machine from outside, a first time calculating part to obtain time, a third processing part to judge whether the time obtained by the first time calculating part meets the expiration date, and a sixth communication part to output a first command when the time meets the expiration date, the first command restricting control of the first control device, and to output a second command when the time does not meet the expiration date, the second command allowing the first control device to be controlled without restriction.

A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.

A system and method for validating unsupervised machine learning models. The method includes: analyzing, via unsupervised machine learning, a plurality of sensory inputs associated with a machine, wherein the unsupervised machine learning outputs at least one normal behavior pattern of the machine; generating, based on the at least one normal behavior pattern, at least one artificial anomaly, wherein each artificial anomaly deviates from the at least one normal behavior pattern; injecting the at least one artificial anomaly into the plurality of sensory inputs to create an artificial dataset; and analyzing the artificial dataset to determine whether a candidate model is a valid representation of operation of the machine, wherein analyzing the artificial dataset further comprises running the candidate model using the artificial dataset as an input.

Provided are an abnormality detection unit that detects abnormality or stop of at least one of a plurality of storage battery housing devices, a storage device extraction unit that extracts, when the abnormality detection unit detects the abnormality or the stop, one or more storage battery housing devices related to the storage battery housing device in which the abnormality or the stop is detected from among the plurality of storage battery housing devices, and an adjustment unit that determines adjustment of utilization rate of at least one of the one or more storage battery housing devices extracted by the storage device extraction unit.

A system for analysis of operation of a hand-guided working apparatus, wherein the working apparatus includes a working tool and a drive system for driving the working tool, includes an optical recording device configured for recording a temporal succession of images of a working procedure on a workpiece using the working tool, an identification device configured for identifying a temporal succession of operational data values of the drive system during recording of the temporal succession of images, and at least one output device configured for outputting in each case at least one of the recorded images together with at least one identified operational data value correlated in time thereto.