Heterogeneous Fault Diagnosis of Industrial Motors via Multimodel Collaborative Neural Architecture Search

Heterogeneous fault diagnosis scenarios are in evitable in industrial systems due to potential communication issues, sensor failures and different downstream enterprise diagnostic capabilities. Traditional manual models with only a single type structure are difficult to handle heterogeneous inputs with different signal dimensions. This article proposes a multi-model collaborative neural architecture search (MMCNAS) framework for heterogeneous fault diagnosis. Unlike traditional neural architecture search that focuses on parameter-level fine-grained search, a model-level coarse-grained search space is adopted to fully utilize the existing knowledge and architectures, which greatly reduces search time and computational consumption. An avoidance-based diversity search (ADS) strategy is specifically developed for this pattern to avoid repeated combinations of models and positions during the search process. The proposed automated multi-model design and optimization method not only endows intelligent diagnostic systems with universal processing capabilities for heterogeneous signals, but also enables self evolution of the system without the need for time-consuming and labor-intensive manual design for multiple tasks. The proposed method was validated on nine tasks in a real industrial motor production line and achieved over 90% F1 scores.