Gyro Motor State Evaluation and Prediction Using the Extended Hidden Markov Model

Lei Dong,Zhiyong Yang,Benfu Ma,Jianfei Wang,Ling-Ling Li,Ming-Lang Tseng

doi:10.3390/sym12111750

Abstract

This study extracted the featured vectors in the same way from testing data and substituted these vectors into a trained hidden Markov model to get the log likelihood probability. The log likelihood probability was matched with the time–probability curve from where the gyro motor state evaluation and prediction were realized. A core component of gyroscopes is linked to the reliability of the inertia system to conduct gyro motor state evaluation and prediction. This study features the vectors’ extraction from full life cycle gyro motor data and completes the training model to feature the vectors according to the time sequence and extraction to full life cycle data undergoing hidden Markov model training. This proposed model applies to full life cycle gyro motor data for validation, compared with traditional hidden Markov model predictive methods and health condition-trained data. The results suggest precise evaluation and prediction and provide an important basis for gyro motor repair and replacement strategies.

Highlights

Electromechanical equipment fault diagnosis and prediction methods are divided into two categories: model-based ones and data-driven ones [1,2,3,4]
Following the normalization of the full life cycle data, we substituted the data into the full life bability as the vertical coordinate, which been by substituting each cycle trained hidden Markov model (HMM)
This study proposes a time-probability curve model for a gyro motor full life cycle prediction

Summary

Introduction

Electromechanical equipment fault diagnosis and prediction methods are divided into two categories: model-based ones and data-driven ones [1,2,3,4]. The former performs diagnoses and predictions using residuals and the models are based on having definite physics implications; these methods suited the precision analysis extremely well. The building of such mathematical models is comparatively difficult due to actual system complexities. Similar approaches are used to reduce the difficulty level and lead to errors in the described systems. The aforementioned methods are more applicable to the description of complex systems

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Discussion

Conclusion