How Does ESA Work?

Electrical Signature Analysis (ESA) uses the motor’s voltage and current to detect issues anywhere within the motor system. This includes everything from incoming power to internal motor components, and even faults within the driven process itself.

To perform an ESA test, basic motor information is required. This includes the nameplate data: voltage, running speed, full load current, and horsepower (or kilowatts). For more detailed analysis, optional information such as the number of rotor bars and stator slots, bearing numbers, or specific load data (like blade count for pumps or tooth count for gearboxes) can also be entered.

ESA software captures a short time window of voltage and current waveforms and applies a mathematical process called the Fast Fourier Transform (FFT). The FFT converts these waveforms into a frequency-based spectrum, which the software then analyzes to detect abnormalities. This spectral analysis helps identify issues in the power supply, motor control, motor components, or connected mechanical systems.

By tracking these signals over time, ESA supports condition-based and predictive maintenance strategies. It can also help identify active faults that may be causing overheating, system shutdowns, or other performance issues.

Faults within the motor or its connected machine typically show up as changes—or “modulation”—in the  current. On the other hand, problems in the incoming power supply tend to appear as modulation in the voltage waveform.

A key distinction between Motor Current Signature Analysis (MCSA) and ESA is that MCSA analyzes only the current waveform. ESA, in contrast, uses both voltage and current data. This dual-input approach allows ESA to more accurately determine whether a fault originates from the power source or from within the motor system.

ESA software displays the FFT results for voltage and current side by side, making it easier to compare and pinpoint the source of a problem. It also uses proprietary algorithms to perform advanced power quality and motor system diagnostics.

One of ESA’s biggest advantages over traditional vibration analysis is its ability to detect faults earlier. Unlike vibration sensors, which are affected by machine mass, stiffness, and frequency limitations, ESA is not influenced by these physical factors. Since ESA monitors electrical current, which directly reflects the motor’s load and condition, it offers a more consistent and reliable diagnostic signal—regardless of the size or construction of the machinery.