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Predictive maintenance & condition monitoring


Using LTspice to analyse vibration data in condition-based monitoring systems


Simon Bramble, staff field applications engineer at Analog Devices, describes how to use LTspice to analyse the frequency content of vibration data in condition-based monitoring systems in order to give an early warning of motor failure in industrial machinery. The article explains how to take X, Y, and Z plane data from a Microsoft Excel spreadsheet and convert it into a format to which LTspice can apply a Fourier transform to produce a plot of the harmonic content of the vibration data


T


he advancement of digital technology shows no sign of slowing down, pervading every area of our lives. Giving machines intelligence is far from an Orwellian dystopia; it instead yields efficiency improvements in factory automation, as automated feedback loops can reduce direct maintenance time. Industry 4.0 describes the concept of


bringing the advantages of big data to the factory floor. Machines fitted with sensors can monitor their own performance and communicate with each other, enabling them to share the overall workload while providing important diagnostic information to the back office, be it in the same building or on another continent. A quick survey of Analog Devices’ product


offerings shows that ADI is heavily committed to providing solutions for the Industrial Internet of Things (IIoT), namely by offering robust, high performance signal chain components from the sensor to the cloud. One such area of industrial automation is that of condition-based monitoring (CbM), whereby the nominal operating characteristics of a machine are carefully calibrated, then the machinery itself is closely monitored with local sensors. Conditions that deviate from the nominal signal show that the machine needs maintenance. Thus, machines equipped with condition- based monitoring systems can be serviced when they actually need it instead of as part of a relatively arbitrary servicing schedule. The preeminent way to determine the state of health of a motor is to examine its vibration signature. Analog Devices’ MEMS technology enables the vibration signature of a motor to be continuously monitored, revealing the health of the motor when its signature is compared to a known no-fault motor. Indeed, each motor fault has its own unique harmonic signature. By looking at the harmonic content of the vibration pattern, faults can be detected in the bearings, inner and outer races, and even in the gearbox teeth.


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Figure 1. Channels X, Y, and Z measured vibration in the side-to-side, vertical, and fore-to-aft directions, respectively.


ANALYZING VIBRATION DATA IN LTSPICE To produce data for Fourier analysis in LTspice, three ADXL1002 accelerometers were connected to a motor, as shown in Figure 1, to measure vibration in the side-to-side, ver tical, and fore-to-aft directions—X, Y, and Z, respectively. The vibration data was downloaded and


saved in a Microsoft Excel spreadsheet. The data was sampled at 500 kSPS, so one second of vibration data resulted in three columns of Microsoft Excel data, each 500,000 lines long. A sample of the X, Y, and Z data is shown in Figure 2. The harmonic content of this data can


now be examined to determine the state of health of the motor. Fourier analysis is the mathematical process of extracting the component frequency content from a waveform. The spectral content of a pure sine wave consists of only one frequency, called the fundamental. If the sine wave is distorted, other frequencies aside from the fundamental appear. By analyzing the frequency content of the vibration pattern of the motor, an accurate diagnosis of its state of health can be determined.


Figure 2. An extract of the X, Y, and Z data. October 2020 Instrumentation Monthly


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