An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations

Li, Yaping; Li, Haiyan; Chen, Zhe; Zhu, Ying

doi:10.3390/en15051685

Cited by 7 publications

(5 citation statements)

References 48 publications

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“…For a comprehensive review of HMM, please refer to [54] and the references therein. Change point detection for HMM arises from the monitoring complex dynamic systems [55], such as communication networks [56], power plants [57], healthcare monitoring [58], manufacture process monitoring [59], distributed machine learning systems, etc.…”

Section: Connection To Hmmmentioning

confidence: 99%

Model-Free Change Point Detection for Mixing Processes

Chen,

Gupta,

Sun

et al. 2024

IEEE Open J. Control. Syst.

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This paper considers the change point detection problem under dependent samples. In particular, we provide performance guarantees for the MMD-CUSUM test under exponentially α, β, and fast ϕ-mixing processes, which significantly expands its utility beyond the i.i.d. and Markovian cases used in previous studies. We obtain lower bounds for average-run-length (ARL) and upper bounds for average-detection-delay (ADD) in terms of the threshold parameter. We show that the MMD-CUSUM test enjoys the same level of performance as the i.i.d. case under fast ϕ-mixing processes. The MMD-CUSUM test also achieves strong performance under exponentially α/β-mixing processes, which are significantly more relaxed than existing results. The MMD-CUSUM test statistic adapts to different settings without modifications, rendering it a completely data-driven, dependence-agnostic change point detection scheme. Numerical simulations are provided at the end to evaluate our findings.

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Section: Connection To Hmmmentioning

confidence: 99%

Model-Free Change Point Detection for Mixing Processes

Chen,

Gupta,

Sun

et al. 2024

IEEE Open J. Control. Syst.

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“…Its dynamic Bayesian network structure is relatively simple, but it can capture complex patterns of temporal dependence between observable variables and latent (unobservable) variables [82]. It is developed on the basis of the Markov chain, which is a discrete memoryless random process responsible for describing the relationship between the sequence of states of the next moment with the current one [83][84][85]. An HMM is an evolution of a Markov chain that requires two stochastic processes, adding a random relationship between the sequence of states and the observation vector, and where the sequence of states cannot be directly observed [83,84,[86][87][88][89].…”

Section: Hidden Markov Models (Hmms)mentioning

confidence: 99%

“…It is developed on the basis of the Markov chain, which is a discrete memoryless random process responsible for describing the relationship between the sequence of states of the next moment with the current one [83][84][85]. An HMM is an evolution of a Markov chain that requires two stochastic processes, adding a random relationship between the sequence of states and the observation vector, and where the sequence of states cannot be directly observed [83,84,[86][87][88][89]. Then, an HMM is a probabilistic time series model, doubly stochastic, which includes the transition of hidden states and emitting observations [90].…”

Section: Hidden Markov Models (Hmms)mentioning

confidence: 99%

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

et al. 2023

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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.

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“…e manufacturing industry is the mainstay of China's economy [25,26]. e YRD is China's largest economic zone and one of the important manufacturing areas in China [27].…”

Section: Case Analysismentioning

confidence: 99%

Evaluation of High-Quality Development of Manufacturing Industry Using a Novel Grey Dynamic Double Incentive Decision-Making Model

Yang

et al. 2022

Mathematical Problems in Engineering

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This paper proposes a novel grey dynamic double incentive decision-making model to evaluate the high-quality development of manufacturing industry. First, we define the concepts of the improved grey incidence analysis and power weight Heronian aggregation (PWHA) operator. Then, we present the double incentive factors and determine incentive static evaluation values. In addition, we construct the weight vector of the time series. Guided by the incentive static evaluation values and weight vector of the time series, the dynamic evaluation values are produced. Finally, a practical example of the manufacturing industry in the Yangtze River Delta (YRD) demonstrates the effectiveness and application of the proposed model.

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An Improved Hidden Markov Model for Monitoring the Process with Autocorrelated Observations

Cited by 7 publications

References 48 publications

Model-Free Change Point Detection for Mixing Processes

Model-Free Change Point Detection for Mixing Processes

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

Evaluation of High-Quality Development of Manufacturing Industry Using a Novel Grey Dynamic Double Incentive Decision-Making Model

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