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Potential, Challenges and Future Directions for Deep Learning in Prognostics and Health Management Applications
Olga Fink, Qin Wang, Markus Svensén, Pierre Dersin, Wan-Jui Lee, Melanie Ducoffe
TL;DR
Deep learning applications in PHM face challenges involving heterogeneous condition-monitoring data and fault-detection tasks. This review evaluates developments, drivers, challenges, potential solutions, and future research needs, highlighting broader practical applicability and combining deep learning with expert knowledge as a research direction.
Problem
Heterogeneous condition-monitoring data at different time scales complicate using deep learning to detect faults or predict impending failures.
Method
The paper provides a thorough evaluation of developments, drivers, challenges, and potential solutions for deep learning applied to PHM.
Results
Deep learning can become applicable to a potentially much wider range of practical tasks.
Takeaways & Limitations
Combining deep learning with expert knowledge is identified as a potentially fertile research direction.
Takeaways & Limitations
Deep learning often requires large computational effort and large labeled datasets that may be difficult or costly to obtain.
Abstract
from arXiv · showhide
Deep learning applications have been thriving over the last decade in many different domains, including computer vision and natural language understanding. The drivers for the vibrant development of deep learning have been the availability of abundant data, breakthroughs of algorithms and the advancements in hardware. Despite the fact that complex industrial assets have been extensively monitored and large amounts of condition monitoring signals have been collected, the application of deep learning approaches for detecting, diagnosing and predicting faults of complex industrial assets has been limited. The current paper provides a thorough evaluation of the current developments, drivers, challenges, potential solutions and future research needs in the field of deep learning applied to Prognostics and Health Management (PHM) applications.
1. Today’s Challenges in PHM Applications
PHM applications must handle heterogeneous, noisy, high-dimensional monitoring data while supporting reliable fault detection, diagnosis, and remaining-useful-life prediction. Deep learning is presented as a potential way to automate feature processing and address limitations of handcrafted feature engineering.
- Data challenges: Complex systems generate heterogeneous condition-monitoring data from many sensor types, time scales, and operating conditions.Signals may include temperature, pressure, flow, vibration, images, and video, and can be affected by noise and redundancy.
- Feature engineering: Raw monitoring data are generally insufficient for detecting faults or predicting impending failures, so PHM commonly relies on feature engineering.Feature engineering transforms signals, applies time-frequency or statistical processing, and may reduce dimensionality through feature selection.
- Feature engineering: Feature selection must balance missed alarms against false alarms while maximizing true-positive detection.Selecting too few or too many features can miss previously unseen fault types, whereas false alarms reduce model credibility.
- Feature engineering: Handcrafted features may fail to distinguish all fault types, transfer across systems, or scale efficiently as monitored parameters increase.Their performance also depends strongly on domain-expert experience and the quality of extracted features.
- Deep learning potential: Deep learning can incorporate at least part of feature engineering into an end-to-end process for PHM applications.The paper highlights automatic processing of massive monitoring data and extraction of useful features from high-dimensional, heterogeneous sources.
- Prognostics: Prognostics remains difficult for deep learning because it concerns future health evolution and estimation of remaining useful life.RUL approaches include model-based, data-driven, knowledge-based, and hybrid directions, with scenarios covering degradation prediction or fault progression.
2. Deep Learning Applications, Successes and Challenges
Deep learning uses multilayer neural networks trained on large datasets, supported by improved algorithms, hardware, and software. Its successes in speech, language, and vision motivate PHM applications, but data and computational demands remain important challenges.
- Introduction to Deep Learning: Deep learning encompasses neural-network variants with multiple computational layers and large numbers of parameters.
- Introduction to Deep Learning: Model architectures often match data structure, with CNNs for images and RNNs for sequential data, helping models identify relevant features during learning.
- Introduction to Deep Learning: Three major drivers of deep learning growth were large labeled datasets, affordable computational hardware, and sophisticated freely available software libraries.
- Challenges and Potential Solutions: Large computational requirements and especially large labeled datasets can be difficult or costly to obtain for many learning problems.
- Challenges and Potential Solutions: Transfer learning can adapt models trained on one task to similar tasks using less data and computational resources.
- Applications and Successes: Deep learning achieved notable advances in speech recognition, machine translation, other NLP tasks, and image recognition after benchmark successes such as AlexNet.
3. DL PHM Applications in Natural Language Processing and Computer Vision
PHM can use deep learning with textual maintenance information and inspection imagery, but these data require specialized processing and may be difficult to label consistently. Applications include end-to-end NLP for maintenance records and CNN-based inspection analysis.
- Natural Language Processing: Failure reports, maintenance records, and event logs contain information about failure frequency, causes, and remedies, but require different processing from numerical sensor data.
- Natural Language Processing: Manually entered failure and maintenance records introduce variability and sometimes ambiguity into PHM data.
- Natural Language Processing: Word embeddings automatically engineer compact features that capture relationships among words and their wider textual context.
- Natural Language Processing: Combining word embeddings with RNNs enables end-to-end learning for PHM NLP, including extracting maintenance keywords and predicting failure types.
- Computer Vision: CNN-based computer-vision systems have been applied to detect defects in railway infrastructure, marine structures, aircraft fuselage, and jet-engine components.
- Computer Vision: Dedicated imaging rigs enabled accurate segmentation of turbine-blade coating loss, which can indicate blade condition.
4. DL PHM applications for sensor condition monitoring data
Deep learning methods for PHM use supervised, unsupervised, and semi-supervised strategies to analyze sensor condition-monitoring data. Approaches include recurrent and convolutional architectures, signal-to-image encodings, reconstruction, clustering, and latent representations, while limited labels and complex signals remain challenges.
- Recurrent structures: Recurrent neural networks, especially LSTMs, have been widely used for PHM applications, with bidirectional LSTMs, GRUs, and attention mechanisms extending this line of work.Attention has improved LSTM performance on time-series data significantly, while attention-based networks have achieved state-of-the-art results on several language tasks.
- Time series to image encoding: Time series can be translated into images so CNN-based image understanding and representation learning can be applied to PHM signals.Methods include natural signal plots, Gramian Angular Fields, Markov Transition Fields, Recurrence Plots, and time-frequency representations.
- 1-D CNN: One-dimensional CNNs use 1-D kernels directly on sensor data for time-series classification, anomaly detection, and remaining-useful-life prediction.This approach has been applied to motor fault diagnosis and broader PHM applications.
- Combinations of LSTM and CNN: CNN-LSTM combinations sequentially extract local features with CNNs before temporal modeling with LSTMs, while ConvLSTM embeds convolution within recurrent-state transitions.ConvLSTM has been applied to anomaly detection and fault diagnosis on multivariate time series.
- Unsupervised and semi-supervised learning: Unsupervised and semi-supervised methods address PHM settings where true lifetimes, system health, or fault labels are unavailable or expensive to obtain.Signal reconstruction learns normal behavior and uses residuals for abnormal-condition detection; autoencoders do not require large amounts of labeled data, while semi-supervised learning uses unlabeled data to improve supervised tasks.
- Unsupervised and semi-supervised learning: Clustering can distinguish fault types when feature spaces are separable, but high-dimensional, noisy, and correlated signals can produce mixed fault-type clusters.Autoencoders can compress condition-monitoring data into a lower-dimensional latent space before clustering is performed there.
5. Promising DL Directions for PHM Applications
Deep learning directions for PHM focus on transferring knowledge across machines and operating conditions, including domain adaptation, transfer learning, and fleet-level approaches. Their industrial use remains constrained by mismatched domains, rare and system-specific faults, and increasing solution complexity.
- 5.1. Transfer Learning: Unsupervised domain adaptation can transfer knowledge from a well-understood machine to another without target-machine labels.The approach is presented as a promising solution to the need for repeated data collection, labeling, and retraining across fleets.
- 5.1. Transfer Learning: Industrial transfer learning is difficult because source and target machines may differ in sensor inputs, fault labels, RUL ranges, and operating conditions.These mismatches violate the identical input- and output-space assumptions common in general domain-adaptation setups.
- 5.1. Transfer Learning: Adversarial distribution-alignment methods have yielded superior performance on many tasks, while direct transfer using ImageNet-pretrained models has shown promising gearbox and induction-motor diagnosis results.The direct-transfer approach converts raw signals into graphical images before fine-tuning image-classification models.
- 5.2. DL for fleet approaches: Fleet PHM must address high variability in system configurations and operating conditions because faults are rare and often unique to specific systems.The paper describes clustering, functional-behavior modeling, and feature-space domain alignment as increasingly complex approaches.
- 5.2. DL for fleet approaches: Fleet approaches face limitations including incomplete aggregated parameters, curse-of-dimensionality effects, similarity requirements, and uncertain future operating-condition representativeness.Increasing methodological complexity can overcome some limitations of earlier approaches but introduces additional constraints.
- 5.4. Deep Reinforcement Learning: Deep reinforcement learning can adjust to new problems quickly by using experience and knowledge gained from solving old problems.The paper also identifies physics-induced machine learning and expert-knowledge integration as directions that may improve model interpretability.
6. Future Research Needs
Future PHM research needs more representative data, methods for heterogeneous domains and evolving environments, and stronger integration of physical or expert knowledge. The paper also identifies unresolved issues in generative modeling, reinforcement learning, uncertainty propagation, interpretability, and industrial collaboration.
- 6. Future Research Needs: Heterogeneous unsupervised domain adaptation is little studied for complex physical systems despite its large potential impact on industrial applications.Heterogeneous fleets may involve different manufacturers and different sensor types, locations, and numbers.
- 6. Future Research Needs: Simulation-to-real adaptation is promising because simulation can provide more sufficient source-domain data for real-life applications.The paper presents simulation environments as one direction for addressing limited industrial data.
- 6. Future Research Needs: Combining deep learning with expert knowledge may support dynamically enriched models and effective digital twins for maintenance decision making.Physics-induced machine learning lacks consensus on its directions and industrial transfer, while further development may improve interpretability.
- 6. Future Research Needs: Representative PHM datasets remain scarce, while insufficient data can impair generalization and increase overfitting risk.Proposed responses include data augmentation, data generation, physics-induced models, and cross-company data sharing; time-series augmentation remains limited.
- 6. Future Research Needs: Generative PHM research must evaluate transferability beyond image-like vibration data and assess the physical plausibility of generated samples.Controlling generated samples according to physical processes remains an open research question.
- 6. Future Research Needs: Future systems need efficient training-data composition and selection as operating conditions evolve and newly measured data may be redundant or informative.The paper connects this challenge with active learning, which selects observations expected to produce the largest performance improvement.