Predictive Maintenance with Electrical Signature Analysis

As a result of targeting more reliable and more sustainable factories by reducing the need for human resource, the increasing needs of the industry, and the costs caused by these needs; machine health and predictive maintenance topics maintain their place among the rising trends of the agenda. The maintenance costs and loss of profit that an unplanned downtime on the production line will cause to the manufacturer can be prevented by monitoring the health of the machines on this line. In this context, investments in predictive maintenance return with the early detection of possible malfunctions.

There are some analysis techniques for monitoring machine health. The most popular among them are vibration analysis, oil analysis, ultrasound analysis, and electrical signature analysis. The diversity of these techniques allows higher accuracy in the diagnosis of the possible malfunctions. While some types of faults can be detected better by vibration analysis, some types of faults are better detected by electrical signature analysis. For instance, electrical signature analysis is used in the detection of broken bar failure, which is the most common rotor failure. Electrical signature analysis can be used as an alternative to vibration analysis in misalignment faults as well.

With electrical signature analysis, faults such as stator faults, rotor faults, bearing faults, load-induced faults can be detected in advance.

Fig. 1 Faults Detected by Different Methods

Electrical Signature Analysis (ESA)

Electrical signature analysis is an umbrella term covering some machine condition monitoring methods that are accomplished by analyzing electrical signals such as current and voltage. Some of these techniques are: Current signature analysis, Voltage signature analysis, Park’s Vector approach, Instantaneous power signature analysis. As a result of these analyzes, reduction in downtime, increase in machine availability, reduction in maintenance costs, better management and planning of maintenance are expected.

With the remote monitoring provided in the facilities where electrical signature analysis is applied, the hazards that the personnel may encounter physically in the monitoring of equipment health are prevented. With electrical signature analysis, the health of any asynchronous motor can be monitored and possible failures can be detected beforehand, without power restrictions. It offers sensitivity in the detection of mechanical faults in motor and load, electrical faults in stator and network problems etc.

Obtaining the Data

Electrical signature analysis does not require sensors mounted on the rotating driveline of the machines. Since the data to be monitored are current and voltage, these measurements can be taken from the phase cables at the motor input. In the motor control center (panel), the phase cables from the driver output to the motor input related to the equipment to be monitored can be detected and current and voltage signals can be obtained with the analog output sensors to be positioned on these cables. Then, by transferring these data to an analyzer or a cloud analysis software via IoT data acquisition devices, the data can be analyzed and possible malfunctions can be detected.

The demands of measurement are precision, accuracy/linearity, high bandwidth, and low power consumption. In order to display the current data, sensors that will reduce the motor current to the input range value of the data collection devices should be used. Sensors such as current transformers, Rogowski coils, Shunt and Trace resistors, Hall-Effect sensors and fluxgate sensors can be used. For voltage measurements, the voltage level is reduced to the appropriate range that analog data acquisition devices can detect with sensors such as voltage/potential transformers, and voltage dividers.

The techniques that make up the electrical signature analysis are used to interpret the obtained data.

Fig. 2 Current Transformer

Motor Current Signature Analysis (MCSA)

MCSA is a method for analyzing and tracking the health of the electrical machine and for detecting the possible malfunctions in the near future with their root causes. It aids in the identification of stator winding issues, rotor issues, coupling issues, load issues, efficiency issues, and bearing issues.

This method employs an asynchronous motor as a transducer, allowing the user to assess the electrical and mechanical conditions. Current signals are taken from the motor and then they are collected by a data acquisition device. After that, the signals are conditioned and spectrum of the signal is obtained. This spectrums are analyzed to detect if there is any matches with fault patterns.

Fig. 3 Monitoring System for Motor Current Signature Analysis

Spectrum of the motor current interprets the frequency components of the current signal with their respective magnitude. In other words, it is the expression of the time waveform signal in frequency domain. This allows patterns in the current signature to be identified, distinguishing healthy motors from unhealthy motors, and even determining which component of the machine may fail. The Fast Fourier Transform is the main instrument in this investigation (FFT).

Voltage Signature Analysis (VSA)

The Voltage Signature Analysis is quite similar with the motor current signature analysis; however, the signal is collected from the motor’s voltage source. It can be beneficial in the examination of stator electrical imbalance along with the analysis of the current signature and the issues caused by motor power in the case of motors. This method is most typically utilized in the analysis of production units.

Instantaneous Power Signature Analysis (IPSA)

Another failure analysis approach based on spectral analysis is instantaneous power analysis. It differentiates from MCSA and VSA with that it evaluates the information contained in the voltage and current signals of a motor phase together, and the demodulated fault component which is named Characteristic Frequency.

The spectrum of the instantaneous power has an extra component which is linked to the modulation induced by the fault, in addition to the fundamental component and sidebands to be formed. This component is known as the Characteristic Component, and it may be used for diagnosing the machine’s status.


Electrical signature analysis has an important role in detecting possible failures. In order to detect these failures, the data must be obtained correctly and in sufficient quantity, then must be analyzed. Sensemore offers its customers the Duck solution, an IoT data acquisition device, to collect and transfer the data to the analysis software. With its 8 channels, the Duck device can take manual, periodic and triggered measurements simultaneously from 8 different analog output sensors. Analog sensors such as current, voltage, temperature, vibration, humidity, pressure etc. can be connected to these channels and the measurements can be viewed in the Sensemore Cloud App.

Fig. 4 Analog IoT Data Acquisition Device: Duck

For electrical signature analysis, Sensemore offers its customers split-core current transformers, Rogowski coils for motors with high currents; and voltage divider sensors to measure voltage values. These are easily placed on the motor phase cables in the electrical control center (panel) and the current-voltage data is transferred to the Duck device. Duck transfers this data to the Sensemore Cloud Application. Thanks to the software in the Cloud Application, this data is analyzed and reports are created for the user about possible failures, also energy monitoring is provided.


  • Bonaldi, E. L., de Oliveira, L. E. D. L., da Silva, J. G. B., Lambert-Torresm, G., & da Silva, L. E. B. (2012). Predictive Maintenance by Electrical Signature Analysis to Induction Motors.

What is Reliability and FMEA?

Ability to prevent failures on a system and a product is the indicator of reliability of the process. Reliability is a key element of a production process that ensures both the production line and the resulting product is existing in the desired state. This element of reliability is created and maintained by the meticulous work of reliability engineering agents such as engineers overseeing the process, maintenance and production personnel.

Concept of reliability is implemented in two distinct forms as proactive and reactive reliability. Proactive reliability relies on foreseeing possible failures and taking appropriate precautions to prevent the occurrence of the failure. Reactive reliability is more of a failure management approach where failures are addressed and eliminated after they come to existence.  A state of ideal reliability is created by detecting and eliminating any possible failure before any occurrence through proactive reliability. Achieving such forecasts for a productive reliability application is possible through conduction of failure analysis. 


A Failure Modes and Effects Analysis (FMEA) is often one of the first steps you would undertake to analyze and improve the reliability of a system or piece of equipment. FMEA is a failure analysis method where every component, assembly and subassembly of a system is observed and studied to determine any possible failure each component might face. Each element is analyzed for its failure modes and impacts of such failures on the whole system. 

In order to evaluate the risk level and the required precautions, assessed elements are given ratings on probability of occurrence, severity of the failure and detection method.


In order to give a component a probability rating analysis,FEM calculations, literature research and comparison to previous failures.

Ratings and their meanings are as such:

Fig. 1 Probability Rating Criteria


In order to give a component a severity rating user has to study the expected and experiences.

Fig. 2 Severity Rating Criteria

Detection Method

The ability and the method of detection for a possible failure is another important aspect of FMEA. Components are assessed according to the ease of failure detection.

Fig. 3 Detection Method Rating Criteria

Additional Assessment Criteria

Potential failure mode , potential cause, mission phase, local effects of failure, next higher level effect, system-level end effect, detection dormancy period, actions for further investigation are all categories of information needed for a complete FMEA.

Impact of Sensemore in Reliability Applications

Solutions developed by Sensemore enables their users to monitor the health of their machinery , constantly keep conditions under supervision, detect and classify different operation and failure modes , predict possible malfunctions before the occurrence and provide a higher level of failure detection ability. 

For FMEA applications, Sensemore solutions provide a great advantage by constantly gathering data about the operation regime, failure modes, root causes of malfunctions and many different types of data such as vibration , temperature, current, voltage etc.. By distinguishing different operation and failure modes Sensemore products enable a more precise assessment for the probability rating part of FMEA. Also through the higher perception power Sensemore products provide their users, rating indicating the difficulty of fault detection is decreased.

Through predictive maintenance and condition monitoring applications such as Sensemore products, conducting proactive reliability analyses such as FMEA is easier and more accurate.