FIELD

This invention relates to the field of machine vibration data analysis. More particularly, this invention relates to a system for analyzing predictive maintenance machine vibration spectra to determine if there is a bearing fault, and if so, determining the type of fault.

BACKGROUND

As shown inFIG. 1, a typical rolling element bearing includes an outer race, an inner race, a cage region disposed between the inner and outer races, and rolling elements (balls) disposed in the cage. Such rolling element bearings have specific bearing failure modes that can be observed in vibration spectral data. Vibration spectral peak frequencies associated with bearing defects are quite different from some other vibration frequencies present in a frequency spectrum of machine vibration because bearing frequencies are defect frequencies. Each defect frequency relates to a particular component of the bearing. For instance, if the Ball Pass Frequency Outer Race (BPFO) defect frequency appears in the spectrum, there is a defect in the outer race of the bearing. If the Ball Spin Frequency (BSF) shows up in the spectrum, there is a defect in one or more of the rollers. This holds true for all of the defect frequencies except for the Fundamental Train Frequency (FTF) or Cage Frequency, which can show up in the frequency spectrum for reasons other than a defect in the cage itself.

Analyzing predictive maintenance machine vibration spectra to determine if there is a bearing fault, and then determining the kind of fault, requires knowledge of the associated fault frequencies for the bearing being monitored. If the relevant bearing properties are not known by the analyst, then accurate bearing fault determination becomes almost impossible.

What is needed is a process that determines the bearing fault frequencies that best match the spectral peaks. This would enable identification of possible bearings having faults. A prerequisite for this process is that there are actual bearing fault spectral peaks in the vibration spectrum (i.e. there is at least the onset of a bearing fault.) Otherwise it is difficult to distinguish bearing fault peaks from general noise peaks.

SUMMARY

Embodiments described herein provide an apparatus and method for identifying machine bearing fault frequencies based on measured machine vibration spectra and thereby identifying which bearings, from an extensive bearing library, match the detected fault frequencies.

Once the bearing fault frequencies have been identified, it is possible to trend the inner, outer, ball spin, and cage interval band energies (summed over all harmonic peaks for each type of energy band) in addition to the typical non-synchronous interval band energy.

A key factor in identifying bearing fault frequencies based on peaks in the vibration spectra is knowledge of the machine rotational speed to a high degree of accuracy. In many situations, particularly with route based portable vibration data collection, machine rotational speed is recorded manually, and therefore is often not sufficiently accurate. A rotational speed algorithm is used to accurately determine the rotational speed.

The above and other needs are met by a vibration measurement and analysis system that identifies one or more faulty bearings in a machine based on spectral vibration data. The system includes one or more vibration sensors, one or more vibration data collectors, and a vibration analysis computer. The vibration sensors are attached to the machine and generate vibration signals based on vibration of the machine. The one or more vibration data collectors include analog-to-digital conversion circuitry and processing circuitry that generates vibration spectral data based on the vibration signals. The vibration spectral data comprises vibration amplitude versus frequency data that includes multiple peak amplitudes at corresponding peak frequencies. At least some of the peak amplitudes are associated with vibration generated by the one or more faulty bearings.

The vibration analysis computer receives the vibration spectral data and executes instructions to:locate the largest peak amplitudes within the vibration spectral data;perform a search of a bearing fault frequency library to generate a list of identified bearings that have bearing fault frequencies that match within a spectral frequency tolerance the peak frequencies of the largest peak amplitudes; anddetermine a normalized accuracy error for each of the identified bearings in the list; andselect from the list one of the identified bearings having the smallest normalized accuracy error.

In some embodiments, prior to selection of the identified bearing having the smallest normalized accuracy error, the vibration analysis computer executes instructions to sort the list of identified bearings based on the normalized accuracy errors.

In some embodiments, the vibration analysis computer determines the normalized accuracy error by executing instructions to:determine a maximum peak amplitude of the largest amplitude peaks;set the spectral frequency tolerance to a selected value;set the normalized accuracy error to an initial value;for each of the largest amplitude peaks and each harmonic of each of the bearing fault frequencies that match the peak frequencies of the largest peak amplitudes, decrease the normalized accuracy error from the initial value by successive calculations of

Er=Er-Ft⁢o⁢l×Ap⁢/⁢Amax|Ff-Fp|,

wherein Eris the normalized accuracy error, Ftolis the spectral frequency tolerance, Apis the peak amplitude, Amaxis the maximum peak amplitude, Ffis the bearing fault frequency or harmonic thereof, and Fpis the peak frequency.

In some embodiments, the vibration analysis computer determines the normalized accuracy error for multiple bearing fault frequency values that include one or more of a Ball Pass Frequency of the Inner race (BPFI), a Ball Pass Frequency of the Outer race (BPFO), a Ball Spin Frequency (BSF), and a Fundamental Train Frequency (FTF).

In some embodiments, the selected value of the spectral frequency tolerance Ftolis at least twice a frequency resolution of the vibration spectral data.

In some embodiments, the vibration analysis computer includes a user interface and executes instructions to automatically select from the list an identified bearing having the smallest normalized accuracy error and communicate the selection to a user via the user interface.

In some embodiments, the vibration analysis computer executes instructions to display the list of identified bearings on the user interface and receive a selection of an identified bearing that is entered by the user via the user interface.

In some embodiments, the vibration analysis computer executes instructions to generate and trend one or more energy band scalar values over a period of time for one or more of a Ball Pass Frequency of the Inner race (BPFI), a Ball Pass Frequency of the Outer race (BPFO), a Ball Spin Frequency (BSF), and a Fundamental Train Frequency (FTF) for the identified bearing having a smallest normalized accuracy error.

In some embodiments, the vibration analysis computer executes instructions to consolidate the list of identified bearings by grouping the identified bearings according to similarity in bearing fault frequencies and number of balls in the identified bearings.

In some embodiments, the one or more vibration data collectors include a portable vibration analyzer or a continuous online vibration monitoring system or both.

In another aspect, embodiments of the invention provide a method for identifying one or more faulty bearings in a machine based on spectral vibration data. The method includes the steps of:(a) generating vibration signals using one or more vibration sensors attached to the machine;(b) generating vibration spectral data based on the vibration signals, wherein the vibration spectral data comprises vibration amplitude versus frequency data that includes peak amplitudes at corresponding peak frequencies, wherein at least some of the peak amplitudes are associated with vibration generated by the one or more faulty bearings;(c) locating the largest peak amplitudes within the vibration spectral data;(d) performing an electronic search of a bearing fault frequency library to generate a list of identified bearings that each has a bearing fault frequency that matches within a spectral frequency tolerance one of the peak frequencies of the largest peak amplitudes;(e) determining a normalized accuracy error for each of the identified bearings in the list; and(f) selecting from the list one of the identified bearings having the smallest normalized accuracy error.

In some embodiments, after step (d) and prior to step (f), the method includes sorting the list of identified bearings based on their normalized accuracy errors.

In some embodiments, step (e) includes:(e1) determining a maximum peak amplitude of the largest amplitude peaks;(e2) setting the spectral frequency tolerance to a selected value;(e3) setting the normalized accuracy error to an initial value;(e4) for each of the largest amplitude peaks and each harmonic of each of the bearing fault frequencies that match the peak frequencies of the largest peak amplitudes, decreasing the normalized accuracy error from the initial value by successive calculations of

Er=Er-Ft⁢o⁢l×Ap⁢/⁢Amax|Ff-Fp|,

wherein Eris the normalized accuracy error, Ftolis the spectral frequency tolerance, Apis the peak amplitude, Amaxis the maximum peak amplitude, Ffis the bearing fault frequency or harmonic thereof, and Fpis the peak frequency.

In some embodiments, the method includes performing step (e4) for multiple bearing fault frequency values that include one or more of a Ball Pass Frequency of the Inner race (BPFI), a Ball Pass Frequency of the Outer race (BPFO), a Ball Spin Frequency (BSF), and a Fundamental Train Frequency (FTF).

In some embodiments, step (e2) includes setting the spectral frequency tolerance Ftolto the selected value of at least twice the frequency resolution of the vibration spectral data.

In some embodiments, step (f) includes automatically selecting from the list an identified bearing having the smallest normalized accuracy error and communicating the selection to a user via a user interface.

In some embodiments, step (f) includes displaying the list of identified bearings on a user interface and receiving a selection of an identified bearing entered by a user via the user interface.

In some embodiments, the method also includes generating and trending one or more energy band scalar values over a period of time for one or more of a Ball Pass Frequency of the Inner race (BPFI), a Ball Pass Frequency of the Outer race (BPFO), a Ball Spin Frequency (BSF), and a Fundamental Train Frequency (FTF) for the identified bearing having the smallest normalized accuracy error.

In some embodiments, the method includes consolidating the list of identified bearings by grouping the identified bearings according to similarity in bearing fault frequencies and number of balls in the identified bearings.

