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Wireless vibration analysis is a powerful tool used to monitor and analyze the vibration characteristics of machinery and structures. By using sensors to measure vibration signals, engineers can identify patterns or anomalies that may indicate a developing problem. This technique is essential for detecting issues early, optimizing maintenance planning, improving equipment reliability, reducing maintenance costs, and enhancing safety. With wireless vibration analysis, engineers can improve the overall efficiency of machinery and ensure a safe working environment. The insights gained from this technique are invaluable for a wide range of industries, including manufacturing, transportation, aerospace, and energy.
Wireless vibration analysis is an essential technique for machinery maintenance and condition monitoring. It provides valuable insights into the health of machinery by detecting changes in vibration patterns that may indicate a developing problem. Here are some of the key reasons why wireless vibration analysis is important for machinery maintenance and condition monitoring:
Early Detection of Problems
Wireless vibration analysis allows engineers to detect machinery problems early, before they become serious and cause costly downtime or even catastrophic failure. By detecting problems early, engineers can take corrective action and prevent more significant damage from occurring.
Improved Maintenance Planning
Vibration analysis provides data that can be used to optimize maintenance planning and scheduling. By identifying the condition of machinery components, engineers can prioritize maintenance activities and schedule them at the most opportune times, minimizing downtime and maintenance costs.
Increased Equipment Reliability
By monitoring the vibration characteristics of machinery, engineers can identify issues that may lead to failure and take corrective action before a failure occurs. This improves the reliability of the equipment and reduces the likelihood of unplanned downtime.
Reduced Maintenance Costs
Wireless vibration analysis can reduce maintenance costs by minimizing the need for reactive maintenance and extending the life of machinery components. By detecting issues early and taking corrective action, engineers can avoid costly repairs and replacements and minimize the total cost of ownership of the equipment.
Enhanced Safety
By monitoring the vibration characteristics of the machinery, engineers can identify issues that may pose a safety risk to personnel. By taking corrective action before a failure occurs, they can prevent accidents and ensure a safe working environment.
Overall, wireless vibration analysis is an essential tool for machinery maintenance and condition monitoring. It provides valuable insights into the health of machinery and enables engineers to optimize maintenance planning, improve equipment reliability, reduce maintenance costs, and enhance safety.
It is an essential tool for predictive maintenance for machines and systems where rotational components are utilized, and it paves the way for utilizing smart, predictive maintenance tools that are the backbone of Industry 4.0.
Vibration measurements are based on three fundamental principles: acceleration, velocity, and displacement. They are the principles used in wireless vibration analysis to measure and analyze the vibration characteristics of machinery and structures, enabling engineers to identify issues early and take corrective action before a failure occurs.
2.1 Acceleration
Acceleration measurements are integral measurements integral to wireless vibration analysis, as they provide vital information on potential issues. Acceleration measuring probes (such as MEMS sensors) are highly sensitive and can detect even small changes in vibrations, which can be an early sign of issues. They have broad frequency response characteristics and can detect a wide range of faults. They are also a preferred method, making them a part of many industry standards for vibration measurements, in addition to being a requirement for equipment to be industry certified.
2.2 Velocity
This is a parameter that is important for quantifying the overall severity of the vibration and its impact on machine function. Velocity sensors have a narrower frequency response compared to accelerometers, which makes them more suitable for measuring mid-frequency vibrations, where they are better suited to identify faults cause by imbalances or misalignment. They are also part of important industry standards and are required for certification of equipment.
2.3Displacement
Displacement measurements are crucial for identifying issues such as misalignment, as they directly measure the movement of the machine under observation. Sensor probes have narrow frequency response and are best suited for measuring low-frequency vibrations that could be caused by structural issues. Additionally, they are used for evaluating potential material deformities in machinery and can prevent structural integrity issues, making them vital for safety.
Direct Time-Based Representation
Time domain analysis provides a direct representation of the vibration signal in the time domain, which can be useful for identifying time-based patterns and transient events, such as impacts or transients.
Easy to Understand
Time domain analysis is relatively easy to understand and interpret, particularly for non-experts. The time waveform can provide a clear visual representation of the vibration signal, making it easier to identify specific events and patterns.
Effective for Detecting Transient Events
Time domain analysis is particularly effective for detecting transient events, such as impacts or transients, that may be missed by frequency domain analysis. These events can be critical for identifying equipment faults and taking corrective action.
Provides Information on Magnitude and Duration
Time domain analysis provides information on the magnitude and duration of vibration events, which can be useful for assessing the severity of equipment faults and determining the appropriate course of action.
Overall, time domain analysis can be a valuable tool for analyzing vibration signals, particularly for identifying transient events and time-based patterns. It provides historic data on how the vibrational performance of the equipment degraded in time and is a good indicator of long-term performance as a whole. It however does not provide detail insights into what specific issues is causing the degraded performance, which is where frequency-domain analysis is more helpful.
3.2. Frequency-domain analysis
Frequency domain analysis can be a great supplement to time-domain analysis where a more detailed examination is required. It has its own advantages and disadvantages, depending on the specific application.
Better Visualization of Frequency Content
Frequency domain analysis provides a more intuitive representation of the vibration signal’s frequency content. The frequency spectrum provides a clear visualization of the amplitudes and frequencies of the vibration components, making it easier to identify specific vibration patterns and frequencies.
Effective for Identifying Specific Frequencies
Frequency domain analysis is particularly effective for identifying specific vibration frequencies, such as those caused by imbalances or misalignments in machinery. The frequency spectrum can help identify dominant frequencies and harmonics, providing valuable insights into the causes of the vibration and the component that is causing the issue.
Ability to Filter Out Noise
Frequency domain analysis can be used to filter out noise and unwanted vibration components from the signal, making it easier to identify the specific frequencies and patterns of interest.
Easier to Compare Data
Frequency domain analysis makes it easier to compare vibration data from different sources, since the frequency spectrum provides a common basis for comparison, further narrowing down the cause of the vibration (to a specific part or subsystem).
Overall, frequency domain analysis excels at identifying specific frequencies and patterns. It works well in conjunction with time-domain analysis in detailing the cause of the vibration and makes identifying and replacing the faulty component quicker, resulting in less downtime.
Frequency-domain is able to provide specific information on what might be causing the increased vibrations, depending on what frequency the peaks are observed at. There are 4 main causes of faults associated with their respective frequencies.
Equipment imbalance can cause vibration in the low-frequency range, typically 1X of the rotation speed of the machine shaft. These faults happen when the center of mass of a machine’s shaft is shifted from its geometrical center, causing radial force, straining the equipment and wearing it.
When two rotating shafts are not parallel (angled) and/or their central axis are shifted a misalignment is present. As is the case with imbalances these result in increased machine vibrations and strain that can cause failure/damage. Usually, a high peak at 2X the rotation frequency of the machine is a good indication of misalignment.
This type of fault presents itself as vibration peaks in frequencies that are integer multiples of the rotation frequency (1X, 2X, …, 10X). These can be cause by improper mounting of the equipment, loose bolts or anchors or defects in the equipment housing.
Most machines utilizing rotating elements use bearings, which themselves have a limited lifecycle and wear over time. Additionally, if the machine works under strained conditions caused by other faults (imbalance, misalignment, looseness) the bearings expired even sooner, which cause vibrations at high frequencies that are non-integer multiples of the shaft rotation speed. These are a good indication that the bearings need replacement, lets a critical failure can occur.
In summary, analyzing the frequencies at which the vibration peaks are gives one data on what particular fault the machine might be experiencing (what is causing the increase in vibrations).
MEMS (Micro-Electro-Mechanical Systems) vibration sensors are based on microfabrication technology and typically include a micro-scale sensing element that can detect changes in acceleration or motion. They have the following advantages over other vibration measurement devices such as Accelerometers, velocity and displacement sensors, etc.
Small Size
MEMS vibration sensors are incredibly small and can be integrated into tiny electronic devices such as smartphones or wearable devices. This makes them ideal for applications where size and weight are critical factors.
Low Power Consumption
MEMS vibration sensors typically consume very little power, making them well-suited for battery-powered applications or devices with limited power budgets (such as most IoT devices).
High Sensitivity
MEMS vibration sensors can detect even the slightest movements or vibrations, making them highly sensitive and accurate. This does not come at the cost of narrow range, making them versatile for many use cases.
Low Cost
MEMS vibration sensors are relatively inexpensive to manufacture compared to other types of vibration sensors, making them a cost-effective option for a wide range of applications.
An example of a good MEMS sensor is the NCD Industrial IoT Wireless Vibration Temperature Sensor V3
The next article in the series is going to discuss the following, giving example measurements in a setup where specific faults are created on purpose. IT will be shown how data can be gathered and analyzed.
I. Data collection
1. How to select appropriate sensor type and mounting location (diagrams on where to mount, how, etc. images of a proper mounting position)
2. Sending the data to a platform for examination (connecting the sensor/modem to Ubidots via Node-RED – done already)
II. Data analysis
1. Visualizing the data in a user-friendly way (have not figured out how to properly parse the x,y,z data yet so it looks good).
2. Identifying peaks in the frequency spectrum (did a bit of calculation already, seems the initial testing round where a compared a 3phase motor with a set of brand-new and worn-down bearings already showed some differences, however they were minor so I will have a greater separation once the testbed is finished)
3. Comparing observed frequencies with known fault frequencies (these already make sense as to what types of faults are expected and are in accordance with the theory in this article)
III. Validating the diagnosis (visual inspection, temp measurement) – not quite sure what to put here, might ask dad for some advice on how to present it if this is even relevant, as the whole point of predictive maintenance is to avoid this.
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