Integrated Fault Detection in AC Induction Motors Using Electrical and Vibration Signals

doi:10.21203/rs.3.rs-3805108/v1

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Integrated Fault Detection in AC Induction Motors Using Electrical and Vibration Signals

https://doi.org/10.21203/rs.3.rs-3805108/v1

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This article emphasizes the importance of identifying faults in critical components, particularly rotor bars. Rotor bars are integral parts of the motor's rotor assembly, and any damage or faults in these components can significantly impact the motor's overall performance. The main focus of the article is to explore a method for detecting faults in AC induction motors, with a specific emphasis on identifying broken rotor faults. The method involves a systematic approach to fault detection that utilizes both electrical and vibration signals generated by the motor.

Vibration is a common phenomenon observed in various natural and man-made systems. It refers to the repetitive back-and-forth or oscillatory motion of an object or system about an equilibrium position. Vibrations can occur in a wide range of scales, from microscopic particles to massive structures, and can be experienced in various forms, including mechanical, acoustic, and electromagnetic vibrations [1–16].

Vibrations can be classified into different types based on their characteristics and underlying mechanisms. Some common types include free vibrations, forced vibrations, damped vibrations, and resonance vibrations [17–26].

Free vibrations occur when a system is set into motion and left to oscillate without any external forces acting on it. This type of vibration is characterized by the natural frequency of the system, which is determined by its mass and stiffness properties [27–36].

Forced vibrations occur when an external force or excitation is applied to a system, causing it to vibrate at a frequency different from its natural frequency. This type of vibration can be periodic or non-periodic, and the response of the system depends on the characteristics of the forcing function and the properties of the system [37–46].

Damped vibrations involve the presence of damping forces that resist the motion of the system, gradually reducing its amplitude and bringing it to a state of equilibrium. Damping can arise from sources such as friction, air resistance, or energy-absorbing materials [47–55].

Resonance vibrations occur when a system is subjected to an external force or excitation at its natural frequency. This leads to a significant amplification of the vibrations and can result in excessive vibrations, which can be detrimental to the system if not properly controlled.

Understanding and analyzing vibrations are crucial in various scientific, engineering, and industrial applications. Engineers use vibration analysis to design structures that are resistant to vibrations, optimize machinery performance, and ensure the safety and reliability of systems. Additionally, researchers use vibration analysis to study the dynamic behavior of materials, characterize the properties of various systems, and develop techniques for vibration control and mitigation.

Forced-damped vibrations are a type of motion exhibited by mechanical systems that are subjected to external forces and also influenced by damping effects. These vibrations occur when a system is forced to oscillate due to the application of an external force, while simultaneously being damped by the presence of resistance or damping factors [56–66].

In a forced-damped vibration scenario, the system experiences an external force that can be periodic or non-periodic. The external force can arise from various sources, such as an applied force, vibrations from machinery, or external disturbances. The system responds to this force by oscillating around its equilibrium position [67–76].

Damping is another crucial factor that affects forced-damped vibrations. Damping refers to the dissipation of energy within the system, which opposes the motion and reduces the amplitude of vibrations over time. Different forms of damping can be present in a system, such as friction, fluid resistance, or energy-absorbing materials. The presence of damping affects the behavior of the system, gradually bringing it to a state of equilibrium and reducing the amplitude of the vibrations [77–86].

AC induction motors have widespread applications across diverse industrial sectors. They serve as fundamental components integral to various industrial processes, contributing significantly to the operation of machinery and systems in different industries.

The passage underscores the critical role of AC induction motors in powering machinery and equipment within industrial processes. The efficiency of these motors is identified as a crucial factor for ensuring the smooth operation of industrial processes.

The efficiency of AC induction motors is highlighted as pivotal for the seamless functioning of industrial operations. The motors are essential for driving machinery and equipment, and their reliable performance directly impacts the overall efficiency of industrial processes.

Reliability is emphasized as a paramount aspect of AC induction motors. The reliable performance of these motors is deemed essential for maintaining operational integrity in industrial settings. The passage stresses the critical need to ensure the continued reliability of these motors.

The passage introduces the concept of proactive maintenance, emphasizing the importance of detecting faults in AC induction motors early on. Detecting faults is identified as a key strategy for maintaining operational integrity, preventing performance issues, avoiding disruptions, and minimizing the potential for costly repairs.

The described method involves an integrated analysis of both electrical and vibration signals generated by the AC induction motor. Electrical signals, comprising voltages and currents from each phase, are considered alongside vibration signals, which include acceleration and velocity signals in different directions. This comprehensive analysis aims to provide a holistic understanding of the motor's condition.

Within the broader context of fault detection, the article narrows its focus to the specific identification of broken rotor faults. Broken rotor bars, and components within the motor's rotor assembly, are highlighted as critical potential faults. Detecting issues with rotor bars is stressed as crucial for preventing further damage and ensuring the continued efficient operation of the motor.

The proposed method relies on the utilization of the Diagnostic Feature Designer app, a dedicated and specialized tool designed for the analysis of signals and extraction of pertinent features. This application serves as a powerful interface, likely in the form of software, that streamlines the complex process of signal analysis for engineers and practitioners working with AC induction motors. The primary function of this app is to simplify and enhance the extraction of meaningful features from the signals obtained from the motor system (see Fig. 1).

The signals subjected to analysis originate from two key sources within the motor system: electrical components and vibration characteristics. The electrical signals comprise voltages and currents, specifically gathered from each of the three phases that constitute the operation of the AC motor. Monitoring these electrical parameters is crucial because variations or irregularities in voltages and currents can serve as indicators of potential issues or faults within the motor, particularly concerning the rotor.

Simultaneously, the app extends its functionality to the realm of vibration signals. Vibration signals provide insights into the mechanical behavior of the motor. These signals encompass acceleration and velocity measurements, capturing the dynamic movements of the motor in three specific directions: radial, axial, and tangential. Radial vibrations typically describe movements along the radius of the motor, axial vibrations along its axis, and tangential vibrations along a tangent to the motor's circumference.

By incorporating both electrical and vibration signals into the analysis, the Diagnostic Feature Designer app enables a comprehensive examination of the motor's health. The extraction of relevant features from these signals is crucial for identifying patterns associated with faults, with a specific emphasis on detecting broken rotor faults. The comprehensive nature of the analysis, covering both electrical and mechanical aspects, provides a more holistic understanding of the motor's condition. Ultimately, this method aims to enhance the accuracy and efficiency of fault detection in AC induction motors, contributing to improved maintenance practices and overall system reliability.

Features extracted from the signals serve as diagnostic indicators of potential faults. These features are likely patterns or characteristics that can reveal motor operation abnormalities. By leveraging both electrical and vibration signals, the analysis aims to provide a comprehensive assessment of the motor's health.

It seems like you are describing a specific analysis of a vibration signal, Vib_acpi, and identifying frequency components of interest in the [900 1300] Hz region. The mention of the readMemberData function suggests that this function is used to compute band-pass filtered variables, namely Vib_acpi_env and Ia_env_ps (see Fig. 2).

From Fig. 2, the power spectrum of the vibration signal Vib_acpi is a graphical representation that reveals the distribution of signal power across different frequencies. Analyzing the power spectrum is a fundamental step in understanding the vibrational characteristics of machinery and extracting diagnostic information related to its operation.

By applying band-pass filtering and subsequently extracting the envelope of signals, it becomes possible to demodulate the signals and focus on lower-frequency components. Analyzing these demodulated signals can reveal important information about the health and behavior of the system, offering valuable insights for diagnostic and monitoring purposes. This technique is commonly employed in the field of signal processing for condition monitoring and fault detection in various systems, including machinery and equipment (see Fig. 3).

After band-pass filtering, the envelope of the signals is extracted. The envelope represents the amplitude variations of the signal over time. It is obtained by capturing the "envelope" or outer shape of the signal, effectively smoothing out the high-frequency oscillations.

Let's break down the key points:

Power Spectrum Analysis: The power spectrum of the vibration signal, Vib_acpi, has been examined. A power spectrum provides information about the frequency content of a signal. In this case, it is observed that there are frequency components of interest within the [900 1300] Hz range.
Frequency Components of Interest: The identified frequency components in the [900 1300] Hz region are deemed significant. These specific frequencies likely contain information relevant to the diagnostic or analytical goals of the study.
Use of readMemberData: The readMemberData function is utilized, and it appears to play a role in computing band-pass filtered variables. The purpose of applying a band-pass filter is often to isolate specific frequency ranges of interest while attenuating others.
Computed Variables - Vib_acpi_env and Ia_env_ps: Two variables, Vib_acpi_env and Ia_env_ps, are mentioned as being computed through the use of the readMemberData function. These variables are likely the results of band-pass filtering applied to the original vibration signal and possibly another signal denoted as Ia.
Band-Pass Filtering Purpose: The band-pass filtering is motivated by the presence of frequency components of interest in the [900 1300] Hz range. By isolating this frequency band, the analysis can focus on specific features or phenomena associated with those frequencies.

It seems like the analysis involves identifying and isolating specific frequency components in the vibration signal Vib_acpi. The readMemberData function is then used to compute band-pass filtered variables, such as Vib_acpi_env and Ia_env_ps, to extract and analyze information within the specified frequency range [900 1300] Hz. This type of analysis is common in signal processing and is often applied in various fields for feature extraction or diagnostic purposes.

The integration of electrical and vibration signals represents a key enhancement to the diagnostic capabilities of the system for assessing the health of an AC induction motor. This integration is designed to leverage the complementary nature of electrical and vibration signals, offering a more comprehensive and nuanced understanding of the motor's condition.

Electrical signals, encompassing voltages and currents, provide insights into the electrical aspects of the motor's operation. Meanwhile, vibration signals, including acceleration and velocity in different directions, offer information about the mechanical behavior of the motor. The combination of these two signal types captures a broader spectrum of information about the motor's overall performance.
Rotor bar faults can manifest in various ways, affecting both the electrical and mechanical components of the motor. By integrating analysis of electrical and vibration signals, the diagnostic system is better equipped to capture diverse manifestations of faults in rotor bars. This inclusivity enhances the ability to detect subtle or complex issues that might not be apparent when examining only one signal type.
The use of both electrical and vibration signals contributes to a more robust and accurate assessment of the motor's health. The integrated analysis allows for a more holistic evaluation, reducing the likelihood of false positives or negatives that might occur if only one type of signal were considered.
The primary goal of this integrated approach is to facilitate early fault detection. By identifying abnormalities or deviations in both electrical and vibration signals, the system can detect potential issues in the rotor bars at an early stage. Early detection is crucial for preventing the escalation of faults, minimizing downtime, and avoiding more severe damage to the motor.
Early fault detection plays a pivotal role in minimizing downtime. When potential issues are identified promptly, corrective measures can be taken before the motor experiences a complete failure. This proactive approach contributes to increased operational reliability and reduced disruptions to the industrial processes reliant on the motor.
The integrated approach not only aids in early fault detection but also informs optimization of maintenance strategies. The diagnostic system can provide insights into the severity and nature of faults, guiding maintenance teams in prioritizing and planning appropriate interventions. This contributes to more efficient and cost-effective maintenance practices.

In conclusion, the described method leverages the Diagnostic Feature Designer app to perform fault detection in AC induction motors, specifically targeting broken rotor faults. By analyzing electrical and vibration signals and extracting relevant features, the diagnostic system offers a comprehensive evaluation of the motor's health. The focus on broken rotor bar detection underscores the practical implications of this approach in ensuring the reliability and longevity of AC induction motors in industrial settings.

Author Contribution

Faouzi Hamdi and Saci Felahi wrote the main manuscript text and prepared figures 1-3. All authors reviewed the manuscript

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No competing interests reported.

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Integrated Fault Detection in AC Induction Motors Using Electrical and Vibration Signals

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Abstract

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1. Introduction

2. Methodology

3. Diagnostic Feature Extraction

4. Benefits of Integrated Analysis

Conclusion

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