Discover how spiderAI™, your assistive copilot for workflows related to condition monitoring, fault diagnosis, reliability engineering, maintenance, and repairs, can bring groundbreaking improvements in decision-making accuracy, productivity, and efficiency.

We are currently releasing it for vibration analysis, allowing maintenance and condition monitoring engineers to independently generate diagnostic reports with world-class expert-level accuracy in just one-tenth of the time previously needed.

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Precisely diagnosing the fault and identifying the root cause within 5 minutes of conversing with spiderAI™

In Chemical Processing Plants, particularly in the catalytic cracking process where gas recycling is vital for maintaining reaction conditions, the failure of the gas recycle fan can halt the entire chemical production process.

During a routine inspection, the fan exhibited an unexpected increase in vibration levels in the 3-4x RPM band on the motor’s NDE horizontal reading. If left unaddressed, this could have led to a complete plant shutdown. Given the initial observations, the fault could be anything.

This conversation with spiderAI™ demonstrates how it precisely diagnosed the fault and identified the root cause using its subject matter expertise and mathematical reasoning, thereby preventing a potentially catastrophic failure.



Routine Inspection and Initial Observation

During a routine site visit, the engineer observed abnormalities in the 3-4x RPM band on the motor’s NDE horizontal reading. Alarmed by the potential issues and the ticking clock, he realized the urgency to diagnose the root cause of the problem accurately before it could cause significant disruption.


Starting Conversation with spiderAI™

With a preliminary suspicion of a mechanical fault, the engineer initiated a conversation with spiderAI™, entering his initial observations. His curiosity was aroused as he aimed to pinpoint the specific type of mechanical fault.


spiderAI™ Requests for More Observations

With the limited observations, spiderAI™ also showed suspicions of few mechanical faults just like an expert would do. Instead of giving an immediate diagnosis, spiderAI™ asked for more specific observations, similar to how a doctor inquires further from a client.Recognizing that detailed information would lead to a more accurate diagnosis, the engineer quickly gathered and shared the additional data, also noting the observations he was unable to collect.


Diagnosis by spiderAI™

In a concise and precise manner, spiderAI™ delivered the diagnosis, revealing the root cause behind the observations. The fault it identified was unexpected, leaving the engineer stunned. The revelation was so surprising that it prompted the engineer to delve deeper into the matter.


Response from Engineer

Surprised, slightly fearful, and intensely curious, the engineer reasoned why this particular fault was identified over other possibilities. He wanted to understand the reasoning behind spiderAI™’s diagnostic conclusion.


Reasoning by spiderAI™

spiderAI™ provided a thorough explanation, detailing the science and mathematical validation with formulae and calculations. Through this communication and reasoning-driven workflow, the engineer was convinced of the root cause. Confident in the accuracy of the diagnosis, he made the correct decision on the necessary maintenance.


Greeting Each Other

The conversation concluded with the engineer and spiderAI™ exchanging greetings, having successfully resolved the issue.

spiderAI™ Thread

Fault Detected: Broken Rotor Bar

Failure Mode Cracked rotor bars


  • Increased electrical resistance
  • Uneven magnetic field distribution
  • Reduced motor efficiency
  • Elevated temperatures
  • Excessive vibration
  • Potential secondary damage to bearings and windings
Broken Rotor Bar Image

Comparing spiderAI™ with GPT-4o

The evaluation of spiderAI™ was conducted to compare its performance with the state-of-the-art model GPT-4 as of June 2024. A vignette, containing a detailed fault profile with various attributes of the fault, was used as the ground truth for evaluation. Conversations were masked to ensure unbiased assessment. Three key metrics—Precision, Plausibility, and Helpfulness—were used to evaluate the responses. Each conversation was carefully reviewed and scored on a scale of 0 to 5 for these metrics. Scores were compiled into a comparison table, and the findings highlighted strengths and areas for improvement for each model. This approach ensured a fair, comprehensive, and objective evaluation of the models' performance.

Metric spiderAI™ (Score) GPT-4o (Score) Reasoning
Precision (Identifying Fault) High (5) Medium (3) spiderAI™ directly identifies rotor bar fault based on symptoms such as harmonics from 1X to 8X RPM and modulation sidebands at 40 CPM. GPT-4o lists rotor bar fault as a possibility but explores other options.
Plausibility (Explanation) High (5) Medium (3) spiderAI™ explains the fault with clear technical reasoning, including harmonics, modulation sidebands at PPF, and the regular beat in the time waveform. GPT-4o provides explanations for various possibilities but lacks depth on specific fault mechanisms.
Helpfulness (Best Course of Suggested Action) High (5) Medium (4) spiderAI™ is assistive in nature, asking for the right observations such as exact RPM, additional frequency peaks, amplitude data, phase data, and historical data. It also offers a detailed and practical course of action, including inspecting rotor bars, conducting MCSA, and monitoring vibration levels. GPT-4o provides helpful suggestions but lacks specificity.

Overall Rating:

spiderAI™: High (15/15)

GPT 4o: Moderate (10/15)

GPT 4o Thread

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