Deep Learning and Its Applications to Machine Health Monitoring: A Survey (1612.07640v1)

Abstract: Since 2006, deep learning (DL) has become a rapidly growing research direction, redefining state-of-the-art performances in a wide range of areas such as object recognition, image segmentation, speech recognition and machine translation. In modern manufacturing systems, data-driven machine health monitoring is gaining in popularity due to the widespread deployment of low-cost sensors and their connection to the Internet. Meanwhile, deep learning provides useful tools for processing and analyzing these big machinery data. The main purpose of this paper is to review and summarize the emerging research work of deep learning on machine health monitoring. After the brief introduction of deep learning techniques, the applications of deep learning in machine health monitoring systems are reviewed mainly from the following aspects: Auto-encoder (AE) and its variants, Restricted Boltzmann Machines and its variants including Deep Belief Network (DBN) and Deep Boltzmann Machines (DBM), Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Finally, some new trends of DL-based machine health monitoring methods are discussed.

• The paper comprehensively surveys deep learning models, including auto-encoders, RBMs, CNNs, and RNNs, to process complex machinery data.
• It demonstrates how these techniques enhance fault diagnosis and predictive maintenance by reducing manual feature engineering.
• The paper highlights future research directions, advocating for transfer learning and integration of domain expertise to improve model performance.

Deep Learning Applications in Machine Health Monitoring

The paper "Deep Learning and Its Applications to Machine Health Monitoring: A Survey" provides a comprehensive overview of the integration of deep learning (DL) techniques in the domain of machine health monitoring systems (MHMS). Given the advent of low-cost sensors and their connectivity through the Internet, DL has emerged as a crucial component in transforming the vast machinery data into operational insights. This survey delineates key DL methodologies applied to MHMS, scrutinizes their applications, and posits future potentials within this domain.

Overview of Deep Learning Techniques

The core focus of the paper lies in DL architectures such as Auto-encoders (AE), Restricted Boltzmann Machines (RBM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN). These techniques have been pivotal in processing and interpreting complex data structures inherent in MHMS. Each model offers unique capabilities; for instance, AEs are proficient in unsupervised feature extraction, RBMs facilitate probabilistic analyses, CNNs excel in pattern recognition, and RNNs are adept at handling temporal dependencies in sequential data.

Applications to Machine Health Monitoring

AE models, particularly stacked denoising auto-encoders, are highlighted for their capability to autonomously extract meaningful features from raw sensor data. This paper describes applications such as fault diagnosis in rotary machinery and hydraulic pump fault identification, where AEs significantly reduce the need for cumbersome feature engineering. Meanwhile, RBMs and DBNs have demonstrated their efficacy in the prognosis, effectively predicting the Remaining Useful Life (RUL) of bearings through learned representations of temporal data.

CNNs offer robust solutions for machine health monitoring by learning invariant features from raw input data, be it 1D time-series or 2D representations like spectrograms. The paper acknowledges significant deployments in fault diagnosis tasks, illustrating CNNs' proficiency in processing vibration and acoustic signals for anomaly detection.

Another cutting-edge approach detailed is the use of RNNs, including their advanced variants like LSTMs and GRUs, for real-time diagnostics that leverage RNNs’ strength in capturing temporal dynamics within machinery data. These models are increasingly used in predictive maintenance, enabling more accurate forecasts of equipment health over time.

Future Directions and Conclusion

The paper advises several promising directions for future research. Notably, it accentuates the necessity for large-scale open-source datasets that can aid in the advancement of DL models. Additionally, incorporating domain knowledge into DL models could enhance their utility and accuracy, suggesting potential intersections between human expertise and algorithmic learning.

Moreover, visualization of DL models and learned data representations may elucidate often opaque DL mechanisms, promoting greater understanding and effective utilization. Transfer learning is also proposed as a valuable strategy, particularly for domains with limited labeled data, allowing models trained on one dataset to be adapted to another related task.

In conclusion, the paper thoroughly elucidates how DL methodologies have become integral to evolving MHMS, providing both theoretical and practical insights. These models streamline complex processes, mitigate dependence on manual feature engineering, and offer scalable solutions across diverse industrial applications. As the landscape of big machinery data expands, DL techniques promise to drive new innovations in machine monitoring, maintenance strategies, and operational efficiency.