Industrial big data analytics for prediction of remaining useful life based on deep learning

CONTACT: PRAVEEN KUMAR. L (, +91 – 9791938249)
MAIL ID: sunsid1989@gmail.com, praveen@nexgenproject.com
Web: www.nexgenproject.com, www.finalyear-ieeeprojects.com
Industrial Big Data Analytics for Prediction of Remaining Useful Life
Based on Deep Learning
Abstract:
Due to recent development of cyber-physical systems (CPSs), big data, cloud computing, and
industrial wireless networks, a new era of industrial big data is introduced. Deep learning,
which brought a revolutionary change in computer vision, natural language processing, and
variety of other applications, has significant potential for solutions providing in sophisticated
industrial applications. In this paper, a concept of device electrocardiogram (DECG) is
presented and an algorithm based on deep denoising auto-encoder (DDA) and regression
operation is proposed for prediction of the remaining useful life of industrial equipment.
First, the concept of electrocardiogram is explained. Then, a problem statement based on
manufacturing scenario is presented. Subsequently, the architecture of proposed algorithm
called integrated deep denoising auto-encoder (IDDA) and algorithm workflow are provided.
Moreover, DECG is compared with traditional factory information system, and the feasibility
and effectiveness of proposed algorithm are validated experimentally. The proposed concept
and algorithm combine typical industrial scenario and advance artificial intelligence, which
has great potential to accelerate the implementation of Industry 4.0.
Existing System:
The experience-based models cannot deal with a large number of queries in expert systems,
and they mostly rely on expert knowledge and engineering experience. The similar deficiency
exits in physics-based models. The physics-based models require insight in system failure
mechanisms, which are supposed to be converted into mathematical expressions.
Proposed System:
The device electrocardiogram (DECG) principle is introduced, and a new methodology based
on deep learning and DECG is proposed for prediction of RUL of equipment as well as
production line. DECG, which is similar to monitor the health of the human body, records
devices’ cycle time with all its sub-processes. Due to much more data collected from DECG,
it’s possible to introduce deep learning and fully enhance the performance of deep learning
for specific application. Based on deep learning and a large number of run-to-failure samples,
the proposed algorithm can provide an accurate prediction of device RUL.

Main Goal:
A concept of DECG in manufacturing environment, which provides a fine-grained status
observation and reduces dependency on experts’ knowledge greatly, is proposed.
A RUL predicting methodology based on regression and deep denoising auto-encoders
(DDA) is proposed to achieve an automatic feature engineering and a high-level features
extraction.
The proposed algorithm and traditional factory information system are compared, and the
experiment is performed in order to validate the feasibility and effectiveness of proposed
algorithm.
Conclusions:
A new algorithm for RUL prediction based on DECG and deep learning is presented. Firstly,
the concept of DECG was introduced. Then, the problem statement in manufacturing
environment was explained. In addition, in order to reduce the impact of experts’ experience
and human decision on prediction, a deep learning methodology, which embraces IDDA and
regression operation, was used. The proposed algorithm was verified by experiments,
wherein DECG was compared with FIS. The experimental result have proven DECG
superiority over FIS in terms of response and reliability. Furthermore, the prediction accuracy
of IDDA was validated by comparison with true RUL. The obtained results have shown a
high effectiveness of proposed algorithm. Nevertheless, the comparison results have indicated
superiority of proposed algorithm and its feasibility to accelerate the implementation of
Industry 4.0.
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Industrial big data analytics for prediction of remaining useful life based on deep learning

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