Endometrium Phase prediction using K-means Clustering through the link of Diagnosis and procedure

Visalaxi, S.; SudalaiMuthu, T

doi:10.1109/spin52536.2021.9566041

Cited by 1 publication

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That is, it minimises the square sum of the distances from each data point to its assigned grouping centre [70]. The Euclidean distance equation is represented below in Equation ( 1) [60,61,69,71].…”

Section: K-means Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

et al. 2023

View full text Add to dashboard Cite

The maintenance paradigm has evolved over the last few years and companies that want to remain competitive in the market need to provide condition-based maintenance (CBM). The diagnosis and prognosis of the health status of equipment, predictive maintenance (PdM), are fundamental strategies to perform informed maintenance, increasing the company’s profit. This article aims to present a diagnosis and prognosis methodology using a hidden Markov model (HMM) classifier to recognise the equipment status in real time and a deep neural network (DNN), specifically a gated recurrent unit (GRU), to determine this same status in a future of one week. The data collected by the sensors go through several phases, starting by cleaning them. After that, temporal windows are created in order to generate statistical features of the time domain to better understand the equipment’s behaviour. These features go through a normalisation to produce inputs for a feature extraction process, via a principal component analysis (PCA). After the dimensional reduction and obtaining new features with more information, a clustering is performed by the K-means algorithm, in order to group similar data. These clusters enter the HMM classifier as observable states. After training using the Baum–Welch algorithm, the Viterbi algorithm is used to find the best path of hidden states that represent the diagnosis of the equipment, containing three states: state 1—“State of Good Operation”; state 2—“Warning State”; state 3—“Failure State”. Once the equipment diagnosis is complete, the GRU model is used to predict the future, both of the observable states as well as the hidden states coming out from the HMM. Thus, through this network, it is possible to directly obtain the health states 7 days ahead, without the necessity to run the whole methodology from scratch.

show abstract

Section: K-means Algorithmmentioning

confidence: 99%

“…To do so, K-means clustering was used. The K-means clustering algorithm followed the following procedure [33,59,65,71,101,107]:…”

Section: K-means Clusteringmentioning

confidence: 99%

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

et al. 2023

View full text Add to dashboard Cite

show abstract

Endometrium Phase prediction using K-means Clustering through the link of Diagnosis and procedure

Cited by 1 publication

References 16 publications

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

Predicting the Health Status of a Pulp Press Based on Deep Neural Networks and Hidden Markov Models

Contact Info

Product

Resources

About