A monitoring device includes a process data acquisition unit configured to acquire process data indicating an operation condition of a machine having a rotating shaft, a shaft vibration value acquisition unit configured to acquire a measurement value of a shaft vibration value of the rotating shaft under the operation condition indicated by the process data, a determination model configured to determine a normal value of the shaft vibration value according to the operation condition created on the basis of the shaft vibration value measured during an operation of the machine and the shaft vibration value calculated on the basis of a predetermined shaft vibration calculation model, and a monitoring unit configured to evaluate the measurement value of the shaft vibration value on the basis of the process data, the measurement value of the shaft vibration value, and the determination model.

A monitoring device includes a process data acquisition unit configured to acquire process data indicating an operation condition of a machine having a rotating shaft, a shaft vibration value acquisition unit configured to acquire a measurement value of a shaft vibration value of the rotating shaft under the operation condition indicated by the process data, a determination model configured to determine a normal value of the shaft vibration value according to the operation condition created on the basis of the shaft vibration value measured during an operation of the machine and the shaft vibration value calculated on the basis of a predetermined shaft vibration calculation model, and a monitoring unit configured to evaluate the measurement value of the shaft vibration value on the basis of the process data, the measurement value of the shaft vibration value, and the determination model.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

Systems and methods for data collection for an industrial heating process are disclosed. The system according to one embodiment can include a plurality of data collectors, including a swarm of self-organized data collector members, wherein the swarm of self-organized data collector members organize to enhance data collection based on at least one of capabilities and conditions of the data collector members of the swarm, and wherein the plurality of data collectors is coupled to a plurality of input channels for acquiring collected data relating to the industrial heating process, and a data acquisition and analysis circuit for receiving the collected data via the plurality of input channels and structured to analyze the received collected data using a neural network to monitor a plurality of conditions relating to the industrial heating process.

A “periodic signal parameter” (PSP) indicates periodic patterns in an autocorrelated vibration waveform and potential faults in a monitored machine. The PSP is calculated based on statistical measures derived from an autocorrelation waveform and characteristics of an associated vibration waveform. The PSP provides an indication of periodicity and a generalization of potential fault, whereas characteristics of the associated waveform indicate severity. A “periodic information plot” (PIP) is derived from a vibration signal processed using two analysis techniques to produce two X-Y graphs of the signal data that share a common X-axis. The PIP is created by correlating the Y-values on the two graphs based on the corresponding X-value. The amplitudes of Y-values in the PIP is derived from the two source graphs by multiplication, taking a ratio, averaging, or keeping the maximum value.

A detector capable of detecting bearing faults in advance is disclosed. The detector includes a microprocessor with an input terminal connected to a power supply and an output terminal connected to a detection information output device. A resonance enhanced piezoelectric sensor is provided. A sensor trigger detection circuit is electrically connected between the sensor and the microprocessor. An input terminal of the sensor trigger detection circuit is connected in parallel with a sensor signal selection circuit. The sensor signal selection circuit is connected in series with a sensor signal processing circuit. An output terminal of the sensor signal processing circuit is connected in series with a programmable gain circuit. The programmable gain circuit is connected to the microprocessor. The sensor trigger detection circuit, the sensor signal selection circuit, the sensor signal processing circuit, and the programmable gain circuit are respectively connected to the power supply.

A detector capable of detecting bearing faults in advance is disclosed. The detector includes a microprocessor with an input terminal connected to a power supply and an output terminal connected to a detection information output device. A resonance enhanced piezoelectric sensor is provided. A sensor trigger detection circuit is electrically connected between the sensor and the microprocessor. An input terminal of the sensor trigger detection circuit is connected in parallel with a sensor signal selection circuit. The sensor signal selection circuit is connected in series with a sensor signal processing circuit. An output terminal of the sensor signal processing circuit is connected in series with a programmable gain circuit. The programmable gain circuit is connected to the microprocessor. The sensor trigger detection circuit, the sensor signal selection circuit, the sensor signal processing circuit, and the programmable gain circuit are respectively connected to the power supply.

Provided is an abnormality detection device which detects an abnormality in a target machine, comprising: a first acquisition unit which acquires a drive side temperature of the target machine; a second acquisition unit which acquires a non-drive side temperature of the target machine; a correlation storage unit which stores a correlation between the drive side temperature and the non-drive side temperature based on the drive side temperature and the non-drive side temperature during normal operation of the target machine; a detection unit which detects a deviation from the correlation stored in the correlation storage unit on the basis of the drive side temperature acquired by the first acquisition unit and the non-drive side temperature acquired by the second acquisition unit; and, an output unit which outputs the deviation from the correlation which was detected by the detection unit as an abnormality in the target machine or as an abnormality indication.

Provided is an abnormality detection device which detects an abnormality in a target machine, comprising: a first acquisition unit which acquires a drive side temperature of the target machine; a second acquisition unit which acquires a non-drive side temperature of the target machine; a correlation storage unit which stores a correlation between the drive side temperature and the non-drive side temperature based on the drive side temperature and the non-drive side temperature during normal operation of the target machine; a detection unit which detects a deviation from the correlation stored in the correlation storage unit on the basis of the drive side temperature acquired by the first acquisition unit and the non-drive side temperature acquired by the second acquisition unit; and, an output unit which outputs the deviation from the correlation which was detected by the detection unit as an abnormality in the target machine or as an abnormality indication.

There is provided a method of specifying a location of occurrence of an abnormal sound, the method including: storing mapping data in a storage device, the mapping data prescribing mapping that receives, as inputs, a sound variable that matches a sound detected in a vehicle and a state variable of a drive-system device of the vehicle synchronized with the sound, and that outputs a location as a main cause of the sound; executing a sound signal acquisition process of acquiring a sound signal output from a microphone that detects a sound; a state variable acquisition process of acquiring the state variable of the drive-system device; and a specifying process of specifying the location of occurrence of the sound corresponding to the sound signal using the sound variable and the state variable as inputs to the mapping. There are also provided a non-transitory storage medium and an in-vehicle device.