Abstract: A method for detecting defects in a rotational element of a machine based on changes in measured vibration energy includes: (a) collecting vibration data over an extended period of time using vibration sensors attached to the machine; (b) processing the vibration data to generate a time waveform comprising processed vibration values sampled during sequential sampling time intervals within the extended period of time; (c) detecting multiple time blocks within the extended period of time during which the processed vibration values exhibit sustained increases at progressively increasing rates; and (d) generating alerts based on detection of the multiple time blocks during which the processed vibration values exhibit sustained increases at progressively increasing rates.

Abstract: A method for detecting defects in a rotational element of a machine based on changes in measured vibration energy includes: (a) collecting vibration data over an extended period of time using vibration sensors attached to the machine; (b) processing the vibration data to generate a time waveform comprising processed vibration values sampled during sequential sampling time intervals within the extended period of time; (c) detecting multiple time blocks within the extended period of time during which the processed vibration values exhibit sustained increases at progressively increasing rates; and (d) generating alerts based on detection of the multiple time blocks during which the processed vibration values exhibit sustained increases at progressively increasing rates.

Abstract: A “store on alert” vibration data acquisition mechanism uses scalar data produced by a vibration monitoring device as a predicate to capturing and storing analytical vibration data in the vibration monitoring device. The scalar data may consist of scalar process variables generated in the vibration monitoring device that are acquired at a fixed interval, such as PeakVue and Overall Vibration. At each interval, these scalar data values are compared to machine performance threshold levels, such as ADVISE, MAINT and FAIL, to determine whether analytical vibration data is to be stored separately inside the vibration monitoring device. Since the analytical vibration data is captured based on a predicate inside the vibration monitoring device (i.e., comparison of the scalar value to the thresholds), the analytical vibration data includes more relevant diagnostic information about a specific machine performance event.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Useful and meaningful machine characteristic information may be derived through analysis of oversampled digital data collected using dynamic signal analyzers, such as vibration analyzers. Such data have generally been discarded in prior art systems. In addition to peak values and decimated values, other oversampled values are used that are associated with characteristics of the machine being monitored and the sensors and circuits that gather the data. This provides more useful information than has previously been derived from oversampled data within a sampling interval.

Abstract: A “store on alert” vibration data acquisition mechanism uses scalar data produced by a vibration monitoring device as a predicate to capturing and storing analytical vibration data in the vibration monitoring device. The scalar data may consist of scalar process variables generated in the vibration monitoring device that are acquired at a fixed interval, such as PeakVue and Overall Vibration. At each interval, these scalar data values are compared to machine performance threshold levels, such as ADVISE, MAINT and FAIL, to determine whether analytical vibration data is to be stored separately inside the vibration monitoring device. Since the analytical vibration data is captured based on a predicate inside the vibration monitoring device (i.e., comparison of the scalar value to the thresholds), the analytical vibration data includes more relevant diagnostic information about a specific machine performance event.

Abstract: Automatically adjusting collection parameters for machines on a route in a collection device, based on states of the machines. For each machine on the route, the machine state is read into the collection device. The machine is included or excluded based on the machine state. The collection device is configured with first collection parameters that are configured based on the machine state. Data is collected from the machine based on the first collection parameters. The data is analyzed using parameters that are configured based on the machine, to determine alerts. Based on the alerts, data is selectively immediately collected from the machine based on second collection parameters that are configured based on the machine state and the alerts. Also based on the alerts, the technician is selectively prompted with the collection device to take a predetermined action and collect data from the machine.

Abstract: A method of automatically adjusting data collection parameters for machines on a data collection route in a portable collection device used by a technician, based on states of the machines. For each machine on the data collection route, the state of the machine is read into the portable collection device. The machine is automatically included in or excluded from the data collection route based at least in part on the state of the machine. The portable collection device is automatically configured with a first predetermined set of data collection parameters that is automatically configured based at least in part on the state of the included machine. An initial set of data is collected from the included machine with the portable collection device, based on the first predetermined set of data collection parameters.

Abstract: 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.

Abstract: Useful and meaningful machine characteristic information may be derived through analysis of oversampled digital data collected using dynamic signal analyzers, such as vibration analyzers. Such data have generally been discarded in prior art systems. In addition to peak values and decimated values, other oversampled values are used that are associated with characteristics of the machine being monitored and the sensors and circuits that gather the data. This provides more useful information than has previously been derived from oversampled data within a sampling interval.