利用 ESA 提高机电可靠性
Figure 1. Common motor faults (CF = center frequency, RS = operating speed, LF = line frequency)
Electrical Characteristic Analysis (ESA) is a predictive maintenance (PdM) technique that uses the motor’s supply voltage and operating current to identify existing and emerging faults throughout the motor system. These measurements act like sensors; any disturbance in the motor system will cause a change (or modulation) in the motor’s supply current. By analyzing these modulations, the source of these motion system disturbances can be identified. Using ESA to test powered-on motors, including AC induction and DC motors, generators, wound-rotor motors, synchronous motors, and machine tool motors used for PdM testing, commissioning, and troubleshooting, can provide valuable information.
Using the portable, handheld, battery-operated ALL-TEST PRO On-Line II™ (ATPOL II™) ESA instrument, current and voltage waveforms are collected, and then technicians can assess the electrical and mechanical condition of the motor system through fast Fourier analysis.
When using ESA technology, motor system faults (whether related to input power, motor electrical or mechanical, mechanical couplings, or drive loads) will have unique characteristics (see Figure 1). Therefore, with information about the motor and motor system, the relevant fault frequencies can be determined, and the entire system can be evaluated.
Numerous performance metrics are displayed in both the time and frequency domains, providing the necessary information to determine the motor’s “health” and the impact of the delivered load. This allows for a real “view” of the actual operating speed, motor slip frequency, gear meshing frequency, transmission system components, and gear speeds.
The Fast Fourier Transform (FFT) is used to create high-frequency and low-frequency spectra. The peaks in these spectra correspond to the rotational speeds of different components in a machine. For example, in the case of a fan driven by a motor via a belt, the peaks correspond to the motor speed, the frequency of magnetic pole passing, the fan speed, and the belt speed. If a gearbox is used instead of a belt drive, then the spectral peaks will appear at the gear speed and the gear meshing frequency.
Perform electrical characteristic analysis
Nameplate data is not required during data collection, but automatic analysis can be performed by inputting the motor nameplate voltage, operating speed, rated power, and full-load current. Common mechanical system failures between the motor and load due to wear and application include belt or direct drive misalignment, belt or sprocket wear, belt tension issues, and pulley wear. Depending on the load type, various failures may occur. The most common are worn parts (such as seals), broken parts (gears, fans, impeller blades, etc.), and bearings.
Using ESA software, technicians can input relevant information about the mechanical system (see Figure 2), and the software will automatically calculate the relevant frequencies (the software provides a cursor to locate these frequencies in the spectrum). Drive equipment analysis includes belt, gear, and blade devices. Note that electrical and mechanical analysis of motors does not require mechanical system information; it is only needed when analyzing mechanical loads.
Figure 2. Electrical signal analysis software can automatically calculate and provide a frequency cursor.
For example, let’s look at the low-frequency data (see Figure 3) of a dust collector fan 1 driven by a 150 kW, 400 V, 260 A, 1485 rpm induction motor. Note the peak marked BLT – this is the belt frequency or belt speed. Multiples of BLT are shown in both spectra. The lower spectrum shows the line frequency peak, as well as sidebands at the BLT frequencies on either side of the line frequency. The presence of the belt frequency, especially at 4.3 A, is significant. The criterion for evaluating sidebands is their presence. Furthermore, the belt frequency is also several times higher, so I suspect there’s a problem with this collector. However, the technicians who collected this data and performed preliminary analysis chose to monitor the machine instead of conducting further inspections or tests.
Figure 3. The dust removal fan is driven by a 150 kW, 400 V, 260 A, 1485 rpm induction motor.
In addition, the sister machine, “Dust Collector Fan 2,” was also tested. In Figure 4, note that the motor load is lower than that of Fan 1 (194A vs. 220A), yet the peak BLT is 8.3A; while Fan 1’s peak is only 4.3A. Based on these preliminary tests, we cannot conclude that this is a serious problem; rather, it is a warning sign that this machine differs somewhat from the first machine.
Figure 4. Test results of dust removal fan 2.
Since this data was acquired during the “inspection” phase of the PdM workflow, the next step is to begin the “analysis” phase. As part of the analysis phase, technicians performed a quick visual inspection of both machines and noticed that the belt on fan 2 was moving excessively compared to the belt on fan 1. The next step involves some additional work, which may include acquiring more data using ESA data or introducing additional instruments during the analysis phase.
Summarize
When using electrical characterization (EMT), electric motors are excellent sensors because you can assess input power, the motor’s electrical and mechanical condition, and the driving load. When it comes to power quality, control, stator and rotor condition, air gap, bearings, alignment, and load, developing faults can be detected and trend analysis performed for predictive maintenance purposes; however, you must first have the right equipment to perform EMT.
This application case is the first part of a three-part series on using ESA to assess the condition of motor-driven mechanical systems.
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