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Columbus, OH, United States

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Equalis Predictive Motor Maintenance (PMM) - Beta Program Summary

October 2, 2017

Abstract

 

Electric motors are used in a wide variety of industrial and transportation applications. Reliability of their operation in most cases is of critical importance and any failures that occur can yield significant costs and downtime, or even lead to disastrous consequences. Traditionally, the problem of ensuring motor reliability is dealt with using a regular time interval of maintenance, whereby trained service personal at a regular time intervals check the machines and their state of operation.  As technology emerges, and control hardware size and power requirements are reduced, we now have the ability to remotely monitor critical motor conditions and report their overall condition back to a system manager thus optimizing the machines up time. 

 

Of the various monitoring techniques commonly employed, vibration measurement remains an effective approach. This is due to the fact that structural failures in a machine system cause changes to the dynamic characteristics of the machine. These changes are reflected in its vibration signals and signatures. By implementing appropriate signal decomposition and representation, features hidden in the vibration signals can be extracted, and an assessment of the machine health status can be made well in advance of a critical machine event horizon.

 

An attractive alternative is now in emergence from Equalis. We have begun to leverage high performance embedded controllers with a low profile accelerometer for vibration measurement and a GSM cellular module to upload data to a server.  This cost effective technology can monitor on a regular, ongoing, basis the mechanical condition of critical motors and thus report on the overall health of a system.  The health is available in simple terms by evaluating the machine spectra signature in terms of a “Green” indicator revealing normal running conditions, a “Yellow” indicator as outside normal, and a “Red” indicator as requiring maintenance within a known and limited time.

 

This approach allows continuous up-to-date information about the state of the critically important equipment, proactive planning for maintenance accordingly, reduce overall costs, minimize undesirable downtime, and prevent unexpected and possibly disastrous equipment failures.

 

Methods & Approach

 

A practical condition-based monitor system designed on this approach involves the following basic objectives;

 

  • Strategic high fidelity motor vibration measurements

  • Collection of the multiple measurements from motors critical in the system

  • Uploading of that data to a remote server via cellular

  • Download and analysis of the collected data

  • Detection and prognosis of motor faults, and generation of motor maintenance plans

 

Equalis has completed a beta (prototype) system design and testing, showing an application that can collect real -time data, and now offers a higher performance package to be used in the field for ongoing deployments. 

 

The beta (prototype) system (Figure 1a & 1b) consists of a commercially available low power microcontroller combined with an accelerometer package that provides an overall update rate of 3.2 kHz., a maximum of 16 G capacity, and draws .1 micro amps in standby mode.  This system communicates with the PC via USB cable and serial data.  It is intended for overall characterization and initial data collection on a NEMA 250 frame motor in lab conditions.

 

Figure 1a - Motors are Equipped with Equalis Motor Monitor Beta System (Prototype) for Characterization and Initial Laboratory Data Collection Tests

 

 

 

 

 

 

Figure 1b

 

 

The embedded controller system was characterized using an ELWE Model U8556001 Vibration Generator combined with a Digital Function / Arbitrary Waveform Generator.  The sensor assembly is securely fastened to the vibration generator shaft.  The waveform generator is set to deliver sine wave signal of a known frequency to the sensor assembly (Figure 1c and d). The PC is loaded with an Equalis serial communication module to collect streamed data from the controller.  The module allows update rate and file size to be adjusted as required.  The data rate and file size is adjusted as required for evaluation.  The unfiltered data is then saved to file and an FFT module is run to produce a spectral signature of the sample.  The FFT signature is then graphed for visualization and analysis.

 

To evaluate overall performance data sets are collected and evaluated beginning at 60 Hz (Figure 1e).  Each data set is increased by 20 Hz and evaluated until the magnitude response is decays -3db down.

 

 

 

 

Figure 1c

 

 

 

 

 

Figure 1d

 

 

 

 Figure 1e

 

 

 

Following the system characterization tests and the calibration data below:

  • 1081 Data Samples

  • 2 mils amplitude = 1200 lines, 4 mils amplitude = 2400 lines

  • 60 hertz = 142 bins

We began tests on the NEMA 250 frame machine.  We collected unfiltered data on one axis of the tri-axial accelerometer and in the radial direction of the motor shaft.  The accelerometer is mounted by center boring and tapping a bearing retainer bolt just adjacent to the single row ball bearing of the AC induction machine (Figure 2a & b).

 

 

 

Figure 2a

 

 

 

 

 

 

 

Figure 2b (Typical Bearing Retainer Cross-section showing hardware)

 

 

Past field and customer experience with cage failures in this machine frame size made data available from the bearing manufacturer to contrast and compare our tests and our spectra model for this type of defect (Figure 3 & Photo 3).  We call attention in Figure 3 to the 2 major peaks in the FFT image.  The larger of the peaks representing the fundamental frequency or machine speed and the smaller peak representing the cage defect as it begins to emerge.

 

 

 

Photo 3

 

 

 

 

Figure 3 (SKF Cage Fracture FFT)

 

 

 

Bearing manufacturers widely publish data of cage and other rolling element defects.  This information reveals that accurate models can be developed and then quantified (Figure 4).  A model of this defect with our drive end bearing yields a cage defect frequency between 35 and 40 % of the fundamental frequency depending on bearing details and speed according to published information.

 

 

 

Figure 4 Rolling Element Bearing (1, 2, 3, 4)

 

 

 

Review of the spectra signature over an entire bearing breakdown time frame reveals multiple stages that occur during the failure process.   These stages may occur over a very substantial period of time (Figure 5).  Our method of inducing one cage defect and then a second served to shorten the process between zones or call out specific zones.  Our data collected was characterized against this type of model.