What are the different approaches to optimizing maintenance schedules based on CBM information and risk considerations?
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- How will AI and machine learning continue to impact CBM? What role will virtual reality (VR) and augmented reality (AR) play in future CBM applications?
- How can domain knowledge be effectively integrated with data-driven CBM approaches? What are the limitations of relying solely on CBM data for maintenance decisions?
- How does CBM improve safety in industrial environments? How does CBM help reduce energy consumption?
- How can government policies and initiatives promote the use of CBM?
- How can a cost-benefit analysis be performed to evaluate the potential return on investment of different CBM approaches?
- What are the challenges of dealing with noisy or incomplete CBM data? How can signal processing techniques improve the quality of CBM data?
- What are the benefits of using deep learning techniques for analyzing complex CBM data (e.g., vibration signals, thermal images)? How can natural language processing (NLP) techniques be used to extract valuable insights from maintenance logs and other textual data related to equipment health?
- What are the legal implications of relying on CBM predictions for critical maintenance decisions? How can collaboration between industry, academia, and government agencies accelerate innovation and adoption of CBM technologies and best practices?
- How can international collaboration foster innovation in CBM? What are the key unanswered questions in the field of CBM?
- What are the challenges of using cloud-based CBM software? How does mobile technology enhance CBM workflows?
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