Order Analysis of a Vibration Signal of a Rotating Machine

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Author(s): Saci Felahi* and Walid Mohamedi

Abstract

Order analysis is a technique used in vibration analysis to identify the sources of vibration in rotating machinery. It helps to determine the frequencies at which various components within the machinery are experiencing vibration. The RPM profile serves as a key parameter in vibration analysis of rotating machinery, allowing for the examination of vibration amplitude changes as a function of rotational speed. By considering these characteristics, analysts can gain valuable insights into the behavior of rotating systems, identify potential faults, and make informed decisions regarding maintenance and repairs.

Introduction

Vibration analysis is a vital technique used to assess the condition, performance, and health of machinery and structures. It involves the measurement, analysis, and interpretation of vibration signals to identify potential issues, such as faults, anomalies, or mechanical problems [1-25].

Vibration is a common phenomenon present in various mechanical systems and structures, ranging from rotating machinery like motors, pumps, and turbines to bridges, buildings, and vehicles. When a mechanical system operates, it generates vibrations that can be caused by factors such as imbalances, misalignments, wear, resonance, structural weaknesses, or other mechanical faults [26-38].

Vibration analysis utilizes specialized sensors, such as accelerometers, to capture the vibrations induced by systems or structures. These sensors measure the acceleration, velocity, or displacement of the vibrating component, converting the mechanical vibrations into electrical signals [39-52].

Once the vibration signals are obtained, they are analyzed using various techniques and tools, including time-domain analysis, frequency-domain analysis, statistical analysis, wavelet analysis, and advanced signal processing algorithms [53-85]. The goal of vibration analysis is to extract meaningful information from the vibration signals to understand the behavior, diagnose any faults or anomalies, and make informed decisions regarding maintenance and repairs.

The key objectives of vibration analysis include:

1. Fault Detection: Identifying the presence of faults or anomalies in machinery or structures. This can involve detecting issues like unbalance, misalignment, bearing wear, gear damage, resonance, or mechanical looseness.
2. Fault Diagnosis: Determining the specific nature, location, and severity of faults. By analyzing the vibrational characteristics, frequency components, and patterns, fault diagnosis aims to pinpoint the source and identify the underlying causes of the problems identified.
3. Condition Monitoring: Regularly monitoring the vibration signals to track changes in machinery performance over time. Condition monitoring enables the identification of gradual deterioration, allowing for predictive maintenance and preventing unexpected failures or breakdowns.
4. Performance Optimization: Assessing the performance of machinery or structures to maximize efficiency, minimize energy consumption, improve productivity, and extend equipment lifespan. Vibration analysis can reveal opportunities for optimization and suggest improvements to enhance performance.
5. Structural Health Monitoring: Evaluating the condition and integrity of structures, such as bridges, buildings, and wind turbines, to ensure they remain safe and operational. Vibration analysis can detect signs of degradation, fatigue, or damage, enabling proactive maintenance and preventing catastrophic failures.

Order analysis is a technique used to analyze the vibration signal of rotating machinery in terms of rotational orders. Rotational orders represent the harmonics of the fundamental rotational frequency of a rotating component (such as a gear or a shaft). Order analysis provides valuable insights into the condition of the rotating components, helps identify specific fault frequencies and aids in fault diagnosis and monitoring.

The first step in order analysis is to measure the vibration signal using an accelerometer or other vibration sensor. This signal is typically recorded over a period of time, during which the machinery is in operation. The signal is then converted from the time domain to the frequency domain using a technique such as the Fast Fourier Transform (FFT).

Next, the frequency spectrum is divided into orders, which represent the harmonics of the rotational speed of the machinery. Each order corresponds to a specific component within the machinery. For example, the first order represents the rotation of the main shaft, while higher orders may represent rotational components such as gears or bearings.

By analyzing the amplitude and phase of each order, it is possible to identify the sources of vibration and determine the severity of the issue. Excessive amplitudes or abnormal phase relationships can indicate problems such as unbalance, misalignment, or bearing faults.

Order analysis can be performed using specialized software or vibration analyzers, which calculate the orders automatically and present the results in a graphical format. This allows maintenance professionals to quickly and accurately diagnose the root cause of the vibration and take appropriate corrective actions.

Order Analysis of a Vibration Signal

Order analysis is a technique used in vibration analysis to understand the sources and characteristics of vibration in rotating machinery. It involves analyzing the frequency content of a vibration signal to identify specific components or phenomena related to the rotational speed of the machinery.

To perform order analysis, the following steps are typically taken:

Data Acquisition: Vibration signals from the machinery are collected using sensors such as accelerometers. The signals are usually collected over a specific time period while the machinery is in operation.

Preprocessing: The collected vibration signal may require preprocessing to remove any noise or unwanted components. This can involve filtering, resampling, or applying mathematical techniques for signal enhancement.

Frequency Analysis: The preprocessed vibration signal is then transformed from the time domain to the frequency domain using techniques like the Fast Fourier Transform (FFT) or wavelet transform. This provides information about the frequency content of the signal.

Order Extraction: In order analysis, the frequency spectrum is then divided into orders. Each order represents a harmonic of the rotational speed of the machinery, with the first order corresponding to the main rotational speed. Higher orders represent harmonics of different components such as gears, belts, or bearings.

Order Tracking: Once the orders are extracted, they can be tracked over time, allowing for the identification of changes or anomalies. This tracking can help pinpoint the specific component or phenomena causing the vibration.

Diagnostic Interpretation: The amplitude and phase characteristics of the orders can provide valuable insights into the condition of the machinery. Excessive amplitudes, changes in phase relationships, or other abnormalities can indicate issues such as unbalance, misalignment, gear wear, or bearing faults.

Reporting and Decision Making: The results of the order analysis are typically presented in graphical or tabular form, allowing maintenance professionals to interpret the data and make informed decisions about necessary corrective actions.

Order analysis is a powerful tool for identifying and diagnosing vibration problems in rotating machinery. By understanding the sources and characteristics of the vibration, maintenance professionals can take appropriate actions to mitigate risks, improve machinery performance, and prevent potential failures.

A motor speed signal commonly consists of a sequence of tachometer pulses. tachorpm can be used to extract an RPM signal from a tachometer pulse signal. tachorpm automatically identifies the pulse locations of a bilevel tachometer waveform and computes the interval between pulses to estimate rotational speed. In this example, the motor speed signal contains the rotational speed, rpm, and hence no conversion is needed.

Plot the motor speed and vibration data as functions of time (see figure 1):

Indeed, when analyzing vibration in rotating machinery, it is common to consider the changes in vibration amplitude as a function of rotational speed. The RPM (Rotations Per Minute) profile, which corresponds to the engine speed during the run-up and coast-down phases, is a crucial parameter in vibration analysis.

Understanding the RPM profile and its influence on vibration amplitudes is crucial in vibration analysis. This knowledge allows analysts to analyze and interpret vibration signals in the context of the machine’s operational conditions and speed variations.

By considering the engine speed and its relationship to vibration characteristics, analysts can distinguish between normal operating conditions and potential faults or irregularities. This analysis approach aids in the identification of specific frequencies associated with faults, detection of resonance effects, understanding gear and bearing behavior, and diagnosing potential issues occurring at specific rotational speeds.

Visualizing Data Using An RPM-Frequency Map

An RPM-frequency map, also known as a Campbell diagram, is a graphical representation used to visualize and analyze the relationship between rotational speeds (RPM) and corresponding frequencies in rotating machinery. It helps in identifying critical speeds and potential resonances within the system.

To create an RPM-frequency map, the following steps can be taken:

Data Acquisition: Vibration data is collected from the rotating machinery using sensors such as accelerometers. This data is typically collected over a range of rotational speeds, covering the operational range of the machinery.

Frequency Analysis: The collected vibration data is transformed from the time domain to the frequency domain using techniques like the Fast Fourier Transform (FFT) or wavelet transform. This provides the frequency content of the vibration signal.

RPM Calculation: From the collected data, the rotational speed (RPM) of the machinery at each measurement point is determined. This can be done using a tachometer or by measuring the number of shaft rotations within a specific time interval.

Frequency vs. RPM Plot: The frequency content of the vibration signal is plotted against the corresponding RPM values. Each data point represents a specific frequency at a specific rotational speed.

Mapping and Analysis: The plotted data forms a graphical representation of the RPM-frequency relationship. This map helps in identifying patterns, trends, and specific frequencies that are critical or resonate with the rotational speeds. Resonance occurs when the frequency of vibration matches the natural frequency of a system, leading to a significant increase in vibration amplitude.

Critical Speed Identification: By analyzing the RPM-frequency map, critical speeds can be identified. These are rotational speeds at which the machinery is prone to experience resonances and vibration issues. It is important to avoid operating the machinery at or near these critical speeds to prevent potential failures or excessive vibrations.

Decision Making and Mitigation: The RPM-frequency map provides valuable insights for maintenance professionals to make informed decisions about potential modifications or countermeasures. This might involve adjusting the machinery’s speed range, installing damping devices, modifying structural components, or implementing balancing techniques to mitigate or avoid resonances.

An RPM-frequency map is a useful tool for visualizing and analyzing the relationship between rotational speeds and frequencies in rotating machinery. By understanding the critical speeds and potential resonances, maintenance professionals can design and operate machinery more effectively to minimize the risk of vibration-related issues and ensure smooth, reliable operation.

Visualizing the vibration data using an RPM-Frequency map facilitates the identification of potential problem areas and aids in the diagnosis of rotating machinery. It provides a comprehensive overview of the machinery’s vibration behavior across different RPM ranges and enables effective decision-making for maintenance, optimization, and ensuring reliable machinery operation (see figure 2).

Determining Peak Orders Using an Average Order Spectrum Determining peak orders using an average order spectrum is a common technique in rotational machinery vibration analysis. The average order spectrum helps identify dominant frequency components related to specific rotational orders, such as gear meshing frequencies or bearing fault frequencies.

En déterminant les ordres de pointe à l’aide d’un spectre d’ordres moyen, les analystes peuvent identifier les composantes de fréquence dominantes associées à des ordres de rotation spécifiques. Cela fournit des informations essentielles pour diagnostiquer les défauts, évaluer l’état des machines tournantes et optimiser les paramètres opérationnels. De plus, la combinaison du spectre d’ordre moyen avec d’autres techniques de diagnostic, telles que l’analyse d’enveloppe ou l’analyse de tendance, peut améliorer les capacités de détection des défauts et de surveillance de l’état des machines tournantes (see figure 3).

The frequency of data collection and the duration over which you analyze peak orders will depend on your business needs. Regularly  reviewing and updating this analysis will help you make informed  decisions to optimize your operations (see Figure 4).

That’s interesting to note that the amplitude of both orders increases as the rotational speed of the motor increases. It suggests a correlation between the motor speed and the order amplitudes. Regarding the capability of “ordertrack” to separate crossing orders when multiple RPM signals are present, it highlights the usefulness of this function for analyzing complex signals.

By utilizing “ordertrack,” you can process and analyze RPM signals, allowing you to separate and identify individual orders even in cases where there are multiple overlapping signals. This can be valuable for understanding the characteristics and behavior of the motor and its components.

To extract time-domain order waveforms for each peak order using “orderwaveform” and compare them to the original vibration signal, you’ll need access to a programming language or software that supports the “orderwaveform” function. Below is a general outline of the steps involved:

• If necessary, preprocess the original vibration signal by removing any noise or irrelevant frequencies. This step helps ensure accurate order waveform extraction.
• Determine the two peak orders for which you want to extract order waveforms.
• Utilize the “orderwaveform” function, passing in the original vibration signal, the specified peak orders, and any other necessary parameters. This function will apply the VoldKalman filter to extract the order waveforms for the specified orders.
• Once you’ve extracted the order waveforms, you can compare them to the original signal. Plotting the waveforms separately or overlaying them on the original signal can help visualize any differences or similarities.
• If desired, you can convert the extracted order waveforms into audio files and play them back to audibly experience the individual peak orders.   

The following figure illustrates order waveforms for peak order

Conclusion

Identify the peaks or prominent frequency components in th (see Figure 5):

average order spectrum. These peaks represent the dominant frequency components for different rotational orders. The order with the highest peak amplitude is likely the fundamental rotational order, while other peaks represent harmonics or sidebands related to gear meshing frequencies, bearing faults, or other phenomena.

In conclusion, order analysis is a valuable tool in the field of vibration analysis, as it provides insights into the sources and severity of vibration in rotating machinery. By identifying and addressing these issues promptly, it is possible to prevent further damage, improve machinery performance, and increase overall reliability [86-88].

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