DCCIL: Mitigating class conflicts in incremental learning through dynamic isolation for intelligent fault diagnosis

Key Points

• Class conflicts in incremental learning lead to significant diagnostic inaccuracies in intelligent systems.
• Dynamic isolation is applied to enhance the learning process, improving fault diagnosis outcomes by 35%.
• Analysis incorporates advanced algorithms for better managing class conflicts while diagnosing faults effectively.
• This approach may enable more reliable systems in real-world scenarios, highlighting the need for further validation.