An evaluation method and an evaluation device for evaluating a health state of well site equipment, and a storage medium are provided. The evaluation method for evaluating the health state of the well site equipment includes: obtaining operating parameters of a component to be tested of equipment to be tested in at least one dimension, the at least one dimension including an operating data dimension, an environmental data dimension, and a maintenance data dimension; obtaining evaluation values of a health state of the component to be tested in various dimensions based on the operating parameters of the various dimensions; obtaining a comprehensive evaluation value of the health state of the component to be tested based on the evaluation values of the health state of the component to be tested in the various dimensions.

A diagnosis device for diagnosing a diagnosis target machine having a rotary machine on the basis of a measured current during rotation of the rotary machine is provided with: an effective value acquisition unit configured to acquire an effective value of the measured current for each specified number of cycles in a target current waveform which is time transition of the measured current in a first predetermined period; a distribution information calculation unit configured to calculate a target distribution information which represents a distribution state of a plurality of the effective values acquired; and a detection unit configured to perform abnormality detection of the diagnosis target machine on the basis of the calculated target distribution information.

There is provided a technique that includes abnormality detecting by picking up a sound generated from a transfer configured to be capable of transporting the substrate and comparing a waveform of sound data with a preset threshold value to detect an abnormality of the transfer; and failure detecting by picking up vibration of the transfer and comparing a waveform of vibration data with a preset threshold value to detect a failure of the transfer.

Provided is a failure diagnostic system for a spacecraft liquid propulsion system that enables to accurately diagnose a failure in a spacecraft and a failure diagnostic method for the spacecraft liquid propulsion system. This spacecraft liquid propulsion system includes a plurality of thrusters, and a supply pipe connected to the thrusters. This system includes a pressure sensor that detects an inner pressure of the supply pipe as time-series data, a frequency spectrum conversion unit that converts the time-series data into data of a frequency spectrum, a storage unit that stores data of a frequency spectrum generated based on an analytical model by computer simulation or a test result of a testing device as a data set, a comparator that compares the data set with the data of the frequency spectrum generated by the frequency spectrum conversion unit, and a determining unit that determines a failure in any one of the plurality of thrusters according to a comparison result of the comparator.

Provided is a failure diagnostic system for a spacecraft liquid propulsion system that enables to accurately diagnose a failure in a spacecraft and a failure diagnostic method for the spacecraft liquid propulsion system. This spacecraft liquid propulsion system includes a plurality of thrusters, and a supply pipe connected to the thrusters. This system includes a pressure sensor that detects an inner pressure of the supply pipe as time-series data, a frequency spectrum conversion unit that converts the time-series data into data of a frequency spectrum, a storage unit that stores data of a frequency spectrum generated based on an analytical model by computer simulation or a test result of a testing device as a data set, a comparator that compares the data set with the data of the frequency spectrum generated by the frequency spectrum conversion unit, and a determining unit that determines a failure in any one of the plurality of thrusters according to a comparison result of the comparator.

Traditionally, benchmarking of asset performance involves comparing actual performance with ideal values that correspond to test conditions which may not be realized in practice leading to inappropriate ranking of the assets. Systems and methods of the present disclosure use condition-aware reference curves for estimating the maximum possible operating efficiencies (under specific operating conditions) instead of the theoretical maximum efficiencies. The reference curves are received from the manufacturer or obtained from on-site test results. Benchmarking is then performed based on two dimensions, viz., an inter-asset metric and an intra-asset metric that are analogous to the first law and second law of thermodynamics respectively. The two-dimensional benchmarking then helps in identifying inefficient assets that may be analyzed further for finding the root cause. Tracking the performance of assets over time greatly helps in operations and maintenance, and thus reducing downtime of systems and accordingly the operating costs.

An accuracy diagnostic device includes a change amount detecting unit, a change amount recording unit, an accuracy change predictor, and an accuracy adjustment timing presenting unit. The change amount detecting unit detects a magnitude of a change in at least one of a state of a machine tool and a surrounding environment of the machine tool as a change amount. The change amount recording unit records the change amount. The accuracy change predictor predicts a future accuracy change of the machine tool using the change amount recorded in the change amount recording unit. The accuracy adjustment timing presenting unit presents a timing at which an accuracy adjustment is to be required for the machine tool based on the accuracy change predicted by the accuracy change predictor.

A state estimation apparatus 1 includes an acquisition unit 2 that acquires deterioration information indicating a deterioration state of each structural object and a learning unit 3 that learns common information that is common between pieces of the deterioration information and estimation index information that is used for estimating a deterioration state of a target structural object, using the deterioration information as input.

An experimentation method for a type I stress intensity factor test considering frost heaving force periodic changes, steps being 1: preparing a specimen, waterjet cutting on the specimen to simulate a non-penetrating rock mass fracture; step 2: vacuum saturating the specimen; step 3: affixing a strain gauge in a non-elastic area at a tip of the specimen; step 4: placing the specimen into a rock mass (1) fracture frost heaving experiment box (5), pressurizing by a pressurizing apparatus (4) balloons on either side of the frost heaving experiment box (5), shutting a valve and removing a pipe, placing the frost heaving experiment box (5) holding the specimen into a water tank, allowing water to immerse the specimen; and step 5: placing the water tank and the frost heaving experiment box (5) holding the specimen together into a high-low temperature alternating experiment box (7) to start a freeze-thaw cycle experiment.

According to one embodiment, a learning apparatus includes a memory and a hardware processor connected to the memory which learns a transformation function to extract a feature value of an input signal. The hardware processor updates the transformation function based on a signal indicative of a first condition and a signal indicative of a second condition which is different from the first condition, using a first loss function on the signal indicative of the first condition and a second loss function on the signal indicative of the second condition. The second loss function is designed such that the second condition becomes distant from the first condition.