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

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.

Application of Hidden Markov Model in Financial Time Series Data

“…Nowadays, there are many research studies in the fields of precision instrument measurement and control, safety and quality inspection, maintenance, and measurement and control. Innovative discoveries provide convenient conditions for testing and management in the engineering and safety fields, so as to better ensure the precision of instruments and the accuracy of construction projects and ensure that high standards and high requirements for production safety and production quality are met [24][25][26][27][28][29].…”

Explaining Data Preprocessing Methods for Modeling and Forecasting with the Example of Product Drying

“…Bununla birlikte daha pek çok yapay zekâ yöntemi bulunmaktadır. Veriye dayalı ikinci yöntem ise istatistiksel öğrenme yöntemleridir (Dong et al, 2020). Mevcut veriyi temsil etmek için ARIMA, SARIMA, SARIMAX, Poisson otomatik gerileyen, doğrusal vb.…”

Bir mekanik jiroskopun yalpalama tahmininde uzun kısa süreli bellek ağı yaklaşımı

“…(2003) (Venkatasubramanian, Rengaswamy, Kavuri, & Yin, 2003). Veriye dayalı yöntem ayrıca iki kategoriye ayrılmaktadır: Biri, sinir ağı ve bulanık mantık tarafından temsil edilen yapay zekâ yöntemleri ve diğeri ise istatistiksel öğrenme yöntemleridir (destek vektör makinesi (SVM) ve gizli Markov modeli (HMM) vs…) (Dong et al, 2020).…”

“…Seyrüsefer sisteminde yapay sinir ağları (YSA) kullanılarak filtre algoritmasının doğruluğu artırılmıştır (Jamil & Kim, 2019). HMM kullanılarak jiroskop motorunun durum değerlendirmesi ve tahmini yüksek hassasiyetle gerçekleştirilmiştir (Dong et al, 2020). Tek eksenli jiroskopta tahmin yöntemi olarak SVM kullanılmıştır (Miao, Li, & Ye, 2015;Song, Hu, & Zhou, 2017;Xudong, Pengfei, Yuanping, & Xingwu, 2013).…”