Overcoming Adversarial Perturbations in Data-driven Prognostics Through Semantic Structural Context-driven Deep Learning

Zhou, Xingyu; Canady, Robert; Li, Yang; Gokhālé, Anirüddhā

doi:10.36001/phmconf.2020.v12i1.1182

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Adversarial attacks on PHM solutions/algorithms have not been actively studied. Very recently, (Zhou et al, 2020) demonstrated adversarial attacks on Remaining Useful Life (RUL) of turbo fan engines. To the best of our knowledge, this is the only work that related to our work in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…Despite the aforementioned importance, securing PHM solutions from adversarial attacks has been largely ignored yet. Very recently, Zhou et al (Zhou, Canady, Li, & Gokhale, 2020) demonstrated that deep learning prognostics models are vulnerable to adversarial attacks. To the best of our knowledge, work by Zhou et al was the only one related to PHM algorithms' security.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Adversarial Vulnerability of PHM algorithms – An initial Study

Yan

Yang²,

Qiu³

2021

PHM_CONF

View full text Add to dashboard Cite

In almost all PHM applications, driving highest possible performance (prediction accuracy and robustness) of PHM models (fault detection, fault diagnosis and prognostics) has been the top development priority, since PHM models’ performance directly impacts how much business value the PHM models can bring. However, recent research work in other domains, e.g., computer vision (CV), has shown that machine learning (ML) models, especially deep learning models, are vulnerable to adversarial attacks; that is, small deliberately-designed perturbations to the original samples can cause the model to make false predictions with high confidence. In fact, adversarial machine learning (AML) targeting security of ML algorithms against adversaries, has become an emerging ML topic and has attracted tremendous research attention in CV and NLP. Yet, in the PHM community, not much attention has been paid to adversarial vulnerability or security of PHM models. We contend that the economic impact of adversarial attacks to a PHM model might be even bigger than that to hard perceptual problems and thus securing PHM models from adversarial attacks is as important as the PHM models themselves. Also, PHM models, since the data used by the models are primarily time-series sensor measurements, have their own unique characteristics and deserve special attention in securing them. In this paper we attempt to explore the adversarial vulnerability of PHM models by conducting an initial case study. More specifically, we consider several unique characteristics associated with streaming time-series sensor measurements data in developing attack strategies for attacking PHM models. We hope our initial study here can shed some light on and stimulate more research interests in the area of PHM models’ security.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On Adversarial Vulnerability of PHM algorithms – An initial Study

Yan

Yang²,

Qiu³

2021

PHM_CONF

View full text Add to dashboard Cite

show abstract

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

et al. 2021

View full text Add to dashboard Cite

An increase in unplanned downtime of machines disrupts and degrades the industrial business, which results in substantial credibility damage and monetary loss. The cutting tool is a critical asset of the milling machine; the failure of the cutting tool causes a loss in industrial productivity due to unplanned downtime. In such cases, a proper predictive maintenance strategy by real-time health monitoring of cutting tools becomes essential. Accurately predicting the useful life of equipment plays a vital role in the predictive maintenance arena of industry 4.0. Many active research efforts have been done to estimate tool life in varied directions. However, the consolidated study of the implemented techniques and future pathways is still missing. So, the purpose of this paper is to provide a systematic and comprehensive literature survey on the data-driven approach of Remaining Useful Life (RUL) estimation of cutting tools during the milling process. The authors have summarized different monitoring techniques, feature extraction methods, decision-making models, and available sensors currently used in the data-driven model. The authors have also presented publicly available datasets related to milling under various operating conditions to compare the accuracy of the prediction model for tool wear estimation. Finally, the article concluded with the challenges, limitations, recent advancements in RUL prognostics techniques using Artificial Intelligence (AI), and future research scope to explore more in this area.

show abstract

On Adversarial Vulnerability of PHM algorithms: An Initial Study

Yan¹,

Yang²,

Qiu³

2021

Preprint

View full text Add to dashboard Cite

With proliferation of deep learning (DL) applications in diverse domains, vulnerability of DL models to adversarial attacks has become an increasingly interesting research topic in the domains of Computer Vision (CV) and Natural Language Processing (NLP). DL has also been widely adopted to diverse PHM applications, where data are primarily timeseries sensor measurements. While those advanced DL algorithms/models have resulted in an improved PHM algorithms' performance, the vulnerability of those PHM algorithms to adversarial attacks has not drawn much attention in the PHM community. In this paper we attempt to explore the vulnerability of PHM algorithms. More specifically, we investigate the strategies of attacking PHM algorithms by considering several unique characteristics associated with time-series sensor measurements data. We use two real-world PHM applications as examples to validate our attack strategies and to demonstrate that PHM algorithms indeed are vulnerable to adversarial attacks.

show abstract

Overcoming Adversarial Perturbations in Data-driven Prognostics Through Semantic Structural Context-driven Deep Learning

Cited by 3 publications

References 16 publications

On Adversarial Vulnerability of PHM algorithms – An initial Study

On Adversarial Vulnerability of PHM algorithms – An initial Study

Data-Driven Remaining Useful Life Estimation for Milling Process: Sensors, Algorithms, Datasets, and Future Directions

On Adversarial Vulnerability of PHM algorithms: An Initial Study

Contact Info

Product

Resources

About