Edge Computing for Predictive Maintenance

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Question ^*

Which type of asset is most critical for predictive maintenance in your operations?

1. a) Rotating Equipment (pumps

2. motors

3. turbines), b) Electrical Infrastructure (transformers, switchgear), c) Fixed Assets (pipelines, tanks, structures), d) Process Equipment (reactors, distillation columns), e) Mobile Assets (vehicles, heavy machinery)

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Question ^*

What level of latency can your predictive maintenance system tolerate?

1. a) Milliseconds (real-time control), b) Seconds (near real-time monitoring), c) Minutes (operational adjustments), d) Hours (scheduled maintenance planning), e) Days (long-term trend analysis)

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Question ^*

How important are the following Edge Computing capabilities to your predictive maintenance strategy? (Rate each on a scale of 1-5, 1 being Not Important, 5 being Extremely Important)

1. Data Preprocessing & Filtering: 1 2 3 4 5

2. Localized AI/ML Inferencing: 1 2 3 4 5

3. Real-time Anomaly Detection: 1 2 3 4 5

4. Secure Data Transmission: 1 2 3 4 5

5. Autonomous Operation (disconnected mode): 1 2 3 4 5

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Question ^*

What is your primary concern regarding deploying edge computing devices in your environment?

1. a) Security vulnerabilities, b) Device management & scalability, c) Environmental robustness (temperature, vibration), d) Power consumption, e) Initial capital expenditure

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Question ^*

What is your biggest challenge in implementing predictive maintenance?

1. a) Lack of real-time data insights, b) High costs of cloud-based solutions, c) Inadequate data security, d) Limited expertise in AI/ML, e) Integrating with existing legacy systems

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