One aspect of the present technology relates to a safety cable vibration monitoring system. The system includes a vibration sensor configured to be coupled to a safety cable. A vibration monitoring computing device is coupled to the vibration sensor. The vibration monitoring computing device includes a processor and a memory coupled to the processor which is configured to execute one or more programmed instructions comprising and stored in the memory to receive data from the vibration sensor. An occurrence of a fall event related to use of the safety cable is determined based on the received data from the vibration sensor. A method of monitoring a safety cable and a safety cable monitoring network are also disclosed.

A machine monitor includes sensors producing a series of scalar values corresponding to sensed physical parameters. An analyzer produces a first database based on the scalar values and determines a median value of the scalar values for each sensor. It also sets a spike level that is offset from the median value by a predetermined multiple of the median value. A spike filter in the analyzer compares the scalar values to the spike level, and identifies a particular scalar value as a potential spike when the particular scalar value differs from the median value by an amount that is equal to or greater than the spike level. A potential spike is determined to be an actual spike if the first and second side values are within a predetermined range of the median value. A second database is produced with the actual spikes eliminated. Using the second database, corrected faults are identified by finding a data point that exceeds a danger level with a preceding data point exceeding a warning level and two trailing data points being less than an advise level.

An abnormality diagnosis system includes a bearing having a plurality of components, a plurality of sensors disposed at different positions of the component and configured to detect surrounding signals, and an abnormality diagnosis device configured to diagnose abnormalities of the rolling bearing and a device disposed around the rolling bearing, based on signals output from the plurality of sensors.

A first determination unit performs a process (first process) of sequentially dividing an intensity value constituting first data by a reference intensity value to calculate an intensity ratio and determining whether the intensity ratio exceeds a first threshold (steps S3 and S4). When a predetermined period has elapsed, a second determination unit calculates an average change rate of the first data in the predetermined period to determine whether the average change rate is within a predetermined range (steps S9 and S10). When it is determined that the average change rate is within the range, the reference intensity value is updated and lowered (step S11). When the reference intensity value is updated, the first determination unit performs the first process by using the updated reference intensity value in the next predetermined period.

Systems and methods for detecting in motion railcar seismic data generated by defective railcar axles of a train traveling on a track. The method uses two or more seismic sensors on the side of the track to capture seismic noise generated by railcar wheels. A wheel that exceeds a preset seismic noise threshold in amplitude, will trigger a wheel tracking algorithm that calculates seismic phase shift data related to the actively tracked wheel noise level, to determine the precise location, in real time, of the faulty wheel carriage while moving. Knowing the precise location of the tracked wheel allows the system to isolate the railcar and capture the railcar and wheel carriage identification information. Subsequently, a railcar log is made on a computer database with the railcar identification information and made available to control centers via ground or satellite links.

Systems and methods for detecting in motion railcar seismic data generated by defective railcar axles of a train traveling on a track. The method uses two or more seismic sensors on the side of the track to capture seismic noise generated by railcar wheels. A wheel that exceeds a preset seismic noise threshold in amplitude, will trigger a wheel tracking algorithm that calculates seismic phase shift data related to the actively tracked wheel noise level, to determine the precise location, in real time, of the faulty wheel carriage while moving. Knowing the precise location of the tracked wheel allows the system to isolate the railcar and capture the railcar and wheel carriage identification information. Subsequently, a railcar log is made on a computer database with the railcar identification information and made available to control centers via ground or satellite links.

A vibration-sensor-integrated vibration exciter 4 has a chassis 21, an excitation unit 22, a magnet 23, a yoke 24, a vibration sensor 25, a fixed plate 26, a moving plate 27, coil springs 28a to 28d, a retaining plate 29, and a crisscross plate 30. Shafts 31a to 31d are fixed to the fixed plate 26. The excitation unit 22 is fixed to the crisscross plate 30. Four vibration-proof rubber members 32a to 32d are installed to the crisscross plate 30 at 90-degree pitches with same radius centering on the excitation axis of the fixed excitation unit 22. The crisscross plate 30 is installed to the retaining plate 29 through the vibration-proof rubber members 32a to 32d. A vibration applied to the chassis 21 is absorbed by the vibration-proof rubber members 32a to 32d, to prevent the yoke 24 from being dislocated in lateral direction due to the vibration.

A sign detection device includes: a plurality of sensors disposed at a plurality of positions on a detection target object and configured to measure physical quantities at each position; a data acquisition unit for acquiring time-series fluctuation data of the physical quantities from the plurality of sensors; a calculation unit for calculating, from the time-series fluctuation data, a parameter indicating a correlation between the physical quantities at arbitrary two positions among the plurality of positions; and a detection unit for detecting a sign of sudden change in vibration of the detection object based on the parameter.

A sign detection device includes: a plurality of sensors disposed at a plurality of positions on a detection target object and configured to measure physical quantities at each position; a data acquisition unit for acquiring time-series fluctuation data of the physical quantities from the plurality of sensors; a calculation unit for calculating, from the time-series fluctuation data, a parameter indicating a correlation between the physical quantities at arbitrary two positions among the plurality of positions; and a detection unit for detecting a sign of sudden change in vibration of the detection object based on the parameter.