Electrical Signature Analysis

In industries where equipment plays a critical role, downtime caused by machine failures can result in significant costs. Electrical Signature Analysis (ESA) offers a proactive approach to machine condition monitoring, helping organizations detect issues early, minimize unplanned downtime, and improve reliability.

This blog will explain what ESA is, how it works, and why it should be an essential part of your maintenance strategy. We’ll also cover the types of faults ESA can detect and how it stands out compared to other techniques.

What is Electrical Signature Analysis (ESA)?

Electrical Signature Analysis (ESA) is a non-intrusive condition monitoring technique that uses current and voltage to detect subtle changes in a machine’s operation. These changes can indicate developing faults, giving you time to schedule maintenance before failure occurs.

ESA differs from other condition monitoring techniques, such as vibration analysis or temperature-based systems, which rely on sensors placed directly on the equipment. Instead, ESA monitors electrical data from a distance, analyzing how the operation of the connected motor affects its magnetic field, and subsequently the current and voltage. This makes it particularly useful for monitoring machines in hazardous or hard-to-reach environments.

The origins of ESA

The roots of ESA can be traced back to the development of Motor Current Signature Analysis (MCSA) by the Oak Ridge National Laboratory (ORNL) in 1985. Initially, the focus was on monitoring motor-operated valves in nuclear power plants, allowing engineers to remotely and non-intrusively gather information from motors in operation. Over time, this led to the development of ESA, which expanded the scope by adding voltage and power monitoring to the analysis.

How ESA works

The process of ESA involves two main stages: data capture and data analysis. Each plays a critical role in detecting faults and improving overall machine reliability.

Step 1: Data capture

The first step in ESA is to install permanent sensors in the motor control cabinet, where they can continuously capture high-frequency electrical data from the machine. ESA’s sensors are different from those used in other condition monitoring systems like vibration analysis, which require sensors to be placed on the machine itself. With ESA, the sensors monitor the machine’s current and voltage remotely, without the need for direct physical access.

This offers several advantages:

• Safety: Sensors are protected from harsh environments, such as high temperatures or hazardous areas.
• Ease of installation: Since the sensors are placed in the motor control cabinet, they are shielded from operational hazards like heat, vibration, or liquid exposure.

ESA sensors attach to the electrical wires in the motor control cabinet, protected from the hazards of the production floor.

Step 2: Data analysis

Once the electrical data is captured, ESA uses a variety of algorithms to analyze it. The most fundamental algorithm in ESA is the Fast Fourier Transform (FFT), which converts time-domain data into the frequency domain. This analysis reveals the machine’s frequency signature, a detailed map of its operational state.

Beyond FFT, ESA uses other methods such as:

• Spectral analysis: Helps to map out the strength of different frequencies in the electrical signal.
• Power analysis: Identifies issues like voltage unbalance and harmonic distortion, which can affect machine performance.
• Lateral and torsional analysis: Provides insights into the machine’s rotational and back-and-forth movements, giving a complete picture of its mechanical health.

Through this continuous data analysis, ESA can identify changes in the machine’s electrical signature that indicate developing faults. ESA’s non-intrusive data collection and comprehensive analysis make it a versatile and reliable tool for condition monitoring.

ESA uses the fast Fourier transform to convert the current and voltage samples into a frequency spectrum. In the bottom graph, the height of each point tells us how much energy that frequency contributes to the corresponding sine wave in the top graph during a short period of time.

Mechanical fault detection with ESA

ESA excels at detecting mechanical faults in machines. Mechanical issues like bearing wear, misaligned couplings, or pump cavitation cause subtle changes in the motor’s operation, which affect the magnetic field and, in turn, the machine’s electrical signature. ESA can detect these changes early by identifying frequency fingerprints specific to certain mechanical faults.

For instance, when a bearing begins to wear, ESA will detect an increase in energy at frequencies associated with the bearing’s physical characteristics. These include:

• Fundamental train (cage) frequency
• Ball pass inner race frequency
• Ball pass outer race frequency
• Ball spin frequency

By monitoring these frequencies, ESA can pinpoint the development of mechanical faults in the drive train, allowing for timely repairs.

ESA can detect and localize mechanical faults in diverse parts of the connected asset.

Electrical fault detection with ESA

ESA is particularly effective at detecting electrical faults, which account for around 30% of motor failures in industrial applications. Electrical problems can have a direct impact on the machine’s magnetic field, and by analyzing current and voltage, ESA provides early detection of these issues.

Some common electrical faults that ESA can identify include:

• Broken rotor bars: These cause imbalances in the motor’s magnetic field, and ESA detects the irregularities in the electrical signature.
• Stator faults: ESA can detect winding insulation issues or short circuits in the stator before they escalate.
• Bearing currents: Stray currents in bearings can cause overheating and premature wear, and ESA identifies these currents early, preventing further damage.

Since ESA measures the machine’s electrical signature directly, it can detect these faults earlier than other condition monitoring systems that focus on vibration or temperature data.

MCSA will detect electrical faults sooner than other techniques.

Additional benefits of ESA: real-time performance & energy monitoring

ESA offers more than just fault detection. It can also provide real-time data on machine performance and energy efficiency, helping to improve operational reliability and reduce energy costs.

Examples of additional benefits include:

• Pump efficiency monitoring: ESA can track how efficiently a pump is operating and help identify areas for improvement, such as reducing cavitation to extend bearing life.
• Power quality monitoring: ESA helps to identify issues like voltage unbalance and harmonic distortion, which can affect both performance and energy usage.
• Energy consumption tracking: Over time, ESA can analyze trends in energy consumption, providing insights into where energy savings can be achieved.

These extra features make ESA a powerful tool for optimizing both machine performance and operational efficiency.