Deep learning for prognostics and health management: State of the art, challenges, and opportunities

AbstractPrognostics and Health Management (PHM), including monitoring, diagnosis, prognosis, and health management, occupies an increasingly important position in reducing costly breakdowns and avoiding catastrophic accidents in modern industry. With the development of artificial intelligence (AI), especially deep learning (DL) approaches, the application of AI-enabled methods to monitor, diagnose and predict potential equipment malfunctions has gone through tremendous progress with verified success in both academia and industry. However, there is still a gap to cover monitoring, diagnosis, and prognosis based on AI-enabled methods, simultaneously, and the importance of an open source community, including open source datasets and codes, has not been fully emphasized. To fill this gap, this paper provides a systematic overview of the current development, common technologies, open source datasets, codes, and challenges of AI-enabled PHM methods from three aspects of monitoring, diagnosis, and prognosis.

Download Full-text

Prognostics and Health Management (PHM) - State-of-the-art in Predictive Maintenance

The International Conference on Electrical Engineering ◽

10.21608/iceeng.2014.30501 ◽

2014 ◽

Vol 9 (9th) ◽

pp. 1-1

Author(s):

Manzar Abbas

Keyword(s):

Health Management ◽

State Of The Art ◽

Predictive Maintenance ◽

Prognostics And Health Management

Download Full-text

Towards Scalable Economic Photovoltaic Potential Analysis Using Aerial Images and Deep Learning

Energies ◽

10.3390/en14133800 ◽

2021 ◽

Vol 14 (13) ◽

pp. 3800

Author(s):

Sebastian Krapf ◽

Nils Kemmerzell ◽

Syed Khawaja Haseeb Khawaja Haseeb Uddin ◽

Manuel Hack Hack Vázquez ◽

Fabian Netzler ◽

...

Keyword(s):

Deep Learning ◽

System Analysis ◽

State Of The Art ◽

Critical Role ◽

Semantic Segmentation ◽

Energy System ◽

Aerial Images ◽

Potential Analysis ◽

3D Data ◽

Challenges And Opportunities

Roof-mounted photovoltaic systems play a critical role in the global transition to renewable energy generation. An analysis of roof photovoltaic potential is an important tool for supporting decision-making and for accelerating new installations. State of the art uses 3D data to conduct potential analyses with high spatial resolution, limiting the study area to places with available 3D data. Recent advances in deep learning allow the required roof information from aerial images to be extracted. Furthermore, most publications consider the technical photovoltaic potential, and only a few publications determine the photovoltaic economic potential. Therefore, this paper extends state of the art by proposing and applying a methodology for scalable economic photovoltaic potential analysis using aerial images and deep learning. Two convolutional neural networks are trained for semantic segmentation of roof segments and superstructures and achieve an Intersection over Union values of 0.84 and 0.64, respectively. We calculated the internal rate of return of each roof segment for 71 buildings in a small study area. A comparison of this paper’s methodology with a 3D-based analysis discusses its benefits and disadvantages. The proposed methodology uses only publicly available data and is potentially scalable to the global level. However, this poses a variety of research challenges and opportunities, which are summarized with a focus on the application of deep learning, economic photovoltaic potential analysis, and energy system analysis.

Download Full-text

Physics Based Deep Learning Technique for Prognostics

Annual Conference of the PHM Society ◽

10.36001/phmconf.2019.v11i1.916 ◽

2019 ◽

Vol 11 (1) ◽

Author(s):

Khaled Akkad

Keyword(s):

Pattern Recognition ◽

Deep Learning ◽

Health Management ◽

Recognition Performance ◽

Remaining Useful Life ◽

Data Driven ◽

Prognostics And Health Management ◽

Learning Technique ◽

Useful Life

Remaining useful life (RUL) estimation is one of the most important aspects of prognostics and health management (PHM). Various deep learning (DL) based techniques have been developed and applied for the purposes of RUL estimation. One limitation of DL is the lack of physical interpretations as they are purely data driven models. Another limitation is the need for an exceedingly large amount of data to arrive at an acceptable pattern recognition performance for the purposes of RUL estimation. This research is aimed to overcome these limitations by developing physics based DL techniques for RUL prediction and validate the method with real run-to-failure datasets. The contribution of the research relies on creating hybrid DL based techniques as well as combining physics based approaches with DL techniques for effective RUL prediction.

Download Full-text

Challenges and Opportunities of System-Level Prognostics

Sensors ◽

10.3390/s21227655 ◽

2021 ◽

Vol 21 (22) ◽

pp. 7655

Author(s):

Seokgoo Kim ◽

Joo-Ho Choi ◽

Nam H. Kim

Keyword(s):

Health Management ◽

System Level ◽

Prognostics And Health Management ◽

Health Index ◽

System Operation ◽

Component Level ◽

Industrial Systems ◽

Multiple Components ◽

Challenges And Opportunities ◽

Essential Function

Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.

Download Full-text