3 Different AI tools Diagnostics on Mechanical Component Photo

I put below same question and photo to three AI tools

Question : Needing to compare AI capabilities in diagnosing mechanical components issues , These two are compressor rotors, What is the problem you can note in this photo ?

ChatGPT answer:

?The image shows a set of helical compressor rotors, and from a visual inspection, here are some possible issues:

1. Varnish or Oxidation Deposits: The rotors have a brownish, glossy appearance, which is indicative of varnish or oxidation deposits from degraded lubricant. This suggests oil degradation, poor filtration, excessive operating temperatures, or an unsuitable lubricant. Varnish can lead to clearance reduction, increased friction, and inefficient compression.
2. Surface Wear or Contact Marks: There appear to be visible wear marks along the rotor lobes, potentially from improper lubrication, misalignment, or contamination. This could lead to efficiency loss, overheating, or mechanical failure.
3. Potential Imbalance or Misalignment: If these rotors were removed due to abnormal vibration or noise, it could be linked to imbalance caused by material buildup, wear, or improper fit.
4. Possible Bearing Journal Wear: The shaft sections (bearing journals) look relatively clean but should be inspected for scoring, fretting, or signs of wear. Worn journals can result in shaft misalignment and increased bearing loads.

Would you like a more detailed analysis regarding lubrication-related failures or other potential failure modes in rotary screw compressors?

谷歌 Gemini:

Based on the provided image, here's a breakdown of potential issues one might note on these compressor rotors, keeping in mind the perspective of AI diagnostics:

Observable Issues (Directly from the Image):

• Wear and Tear: The most prominent observation is the significant wear and tear, especially on the lobes of the rotors. This is evident from the: Loss of Material: The edges and surfaces of the lobes appear worn down, indicating material loss due to friction, abrasion, or potentially corrosion. Pitting and Roughness: The surfaces of the lobes are not smooth. There's visible pitting and roughness, suggesting erosion or possibly cavitation damage. Change in Profile: The intended smooth, curved profile of the lobes seems altered, particularly at the edges. This deviation from the original design can impact performance and efficiency.
• Coloration/Discoloration: The golden-brown hue on parts of the rotors is likely not the original material color. This could indicate: Heat Treatment/Oxidation: It might be a result of heat treatment during manufacturing or repair. However, it could also indicate excessive heat during operation, leading to oxidation or other changes in the metal's properties. Deposits: The color might be due to deposits from the compressed medium (e.g., oil, process gases) or contaminants in the system.
• Potential Imbalance: While not definitively visible from a single static image, the uneven wear could suggest potential imbalance in the rotor assembly. This would need further investigation during inspection.

Issues Inferred (Requiring Further Investigation or Context):

• Clearance Issues: The wear patterns suggest that the clearances between the rotors and the housing (or between the rotors themselves) may be ???? ?? ???? (out of tolerance). This would impact compression efficiency and could lead to further damage.
• Performance Degradation: The observed wear and tear likely translate to reduced compression efficiency and output. The compressor's flow rate and pressure ratio may be compromised.
• Vibration and Noise: Uneven wear and potential imbalance can lead to increased vibration and noise during operation.1
• Root Cause of Wear: Understanding the cause of the wear is crucial. Was it due to: Insufficient Lubrication? Contamination in the Compressed Medium? Corrosion? Excessive Operating Temperatures? Material Fatigue? Manufacturing Defects?

How AI Could Assist in Diagnosis:

• Image Recognition and Analysis: AI algorithms can be trained to detect and quantify wear, pitting, and other surface defects from images with greater precision and consistency than manual inspection.2
• Anomaly Detection: AI could compare the observed wear patterns to historical data or simulations to identify anomalies and predict potential failures.3
• Predictive Maintenance: By analyzing trends in wear and other parameters, AI can help predict the remaining useful life of the rotors and schedule maintenance proactively.4
• Integration with Sensor Data: Combining image analysis with data from vibration, temperature, and pressure sensors can provide a more holistic view of the compressor's condition and improve diagnostic accuracy.

Important Note: This analysis is based solely on the provided image. A complete and accurate diagnosis would necessitate a thorough physical inspection, including dimensional measurements, material analysis, and possibly non-destructive testing (NDT) techniques. Furthermore, understanding the compressor's operating history and application is essential for determining the root cause of the observed issues.

微软 Copiliot:

The image shows two compressor rotors with helical lobes. Here are a couple of issues I can note:

1. Discoloration: There is noticeable discoloration on the surfaces of the lobes. This could indicate overheating or excessive friction during operation.
2. Wear and Tear: The edges of the lobes show signs of mechanical wear, which might suggest prolonged use or improper lubrication.

Identifying these issues early can help maintain optimal performance and prevent costly breakdowns. How do you plan to address these problems?