The Role and Importance of Electrical Signature Analysis in Predictive Maintenance

The initial phase in condition monitoring is basic inspections. Small changes, such as abnormal heat or pressure, strange noises, excessive vibration, or a particular odor, are typically signs that something isn’t working properly. All techniques of condition monitoring are employed in a large variety of systems, from the most basic checks to cutting-edge tools. There are several condition monitoring techniques, and each is tailored to a particular machine component. When checking the state of various points of the machines, it is important to choose and apply the proper condition monitoring techniques according to the point to be applied since different forms of failure can be detected by different condition monitoring techniques.

In addition to electrical signature analysis, there are condition monitoring methods such as oil analysis, ultrasound analysis, infrared thermography, and acoustic emission analysis. Each of them has a different capacity to detect malfunctions that may occur in the machine a while ago. For example, oil analysis and ultrasound techniques can predict possible malfunctions earlier than vibration and electrical signature analysis methods, even when the machine is in good condition and shows no visible signs of malfunction.

As the fault progresses in the machine, the repairment of the fault becomes costlier and requires more time. This means more unexpected downtime. On the other hand, the condition monitoring method to be applied should be an economical solution. Since the measurement devices and application required for condition monitoring with the mentioned oil and ultrasound analysis methods are costly, their application may not be possible everywhere. Besides, each method is specialized in finding certain failure modes. Therefore, vibration analysis and electrical signature analysis methods, which have a wider scope and application, have found more place in predictive maintenance applications.

41 percent of asynchronous motor failures are caused by the bearings, 37 percent by the stators, and 10 percent by the rotors. Although bearing failures can be detected more clearly with vibration analysis, they can also be detected by electrical signature analysis as well. On the other hand, in asynchronous motors, especially in stator and rotor faults, the electrical signature analysis provides a very clear detection and is a very powerful and economical solution compared to other methods.

Mechanical misalignment or unbalance, and air gap misalignment can be detected by both electrical signature analysis and vibration analysis. Failures caused by motor supply and insulation problems can be detected by electrical signature analysis. Electrical signature analysis is much more powerful than other detection methods in detecting broken rotor bars and stator problems.

Since each condition monitoring method can analyze the health status of different parts of the machine, and detect possible faults that may occur in these parts with their root causes, increasing the application of these methods enables the detection of faults in a wider range. Since electrical signature analysis is easy to implement, it can be easily preferred instead of or alongside other condition monitoring methods.

Unlike other condition monitoring methods, electrical signature analysis is not dependent on machine operating conditions to collect data. Because the sensors to collect the data are not applied directly on the machine, but in the cells called the motor control center (MCC) or the panel where the electrical connections of the machine are made. Here, the current and voltage values of the motor are measured by attaching current and voltage sensors to the cables feeding the motor. Since the ambient conditions in the motor control center are generally the same, measurements can be taken continuously, and the data can be analyzed uninterruptedly.

Since current and voltage data are very clear information, these data can be easily obtained accurately by applying economic solutions with basic measurement methods. Making sense of data is also easier compared to other condition monitoring methods. By analyzing the data in the frequency space, the presence of the fault can be detected by the formation of sideband components with equal frequency intervals to be formed around a fundamental frequency (motor driving frequency). The harmonics of the sidebands can also be observed. The main part where electrical signature analysis differs from vibration analysis is that harmonic frequency components in multiples of the fundamental frequency are observed in the vibration analysis, while electrical signature analysis observes sideband and sometimes sideband harmonics.

In addition to collecting and analyzing vibration data with vibration and temperature sensors Infinity and Wired, Sensemore also collects and analyzes electrical data with current/voltage sensors and IoT data collection device Duck. There are 8 different channels for Duck to collect data from different sensors simultaneously and transfer it to the cloud, and 3-phase current and 3-phase voltage information in motors can be collected simultaneously using 6 of these channels. In this way, methods such as motor current signature analysis (MCSA), voltage signature analysis (VSA), and instantaneous power signature analysis (IPSA), among the techniques included in the electrical signature analysis, can be applied.

Current and voltage data are taken from the phase cables feeding the motor via sensors and transmitted to Duck. Duck transfers this data wirelessly to the cloud. Data can be viewed and analyzed via the cloud application. Possible malfunctions that may occur in the machine are notified to the users in advance and it is ensured that the users can take action before unexpected stoppings occur.