Hidden markov models in reliability and maintenance

Gámiz, María Luz; Limnios, Nikolaos; Segovia-García, María del Carmen

doi:10.1016/j.ejor.2022.05.006

Cited by 30 publications

(7 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The present work represents a continuation of research [51], as well as a combination of PHM-CBM, focusing on the development of an automated tool to assist maintenance operations for complex, heterogeneous systems, and data communication networks. Regarding the complexity of automation components, IoT-connected devices and communication networks increase on a day-by-day basis, and the maintenance of heterogeneous systems becomes more and more difficult.…”

Section: Discussionmentioning

confidence: 99%

“…Complex systems and distributed network maintenance have also been the preoccupations of many researchers [48][49][50], and modeling of the present and future states using different models, including Markov Chains and/or Hidden Markov, are discussed in connection with some applications for several systems [51], based on the modeling of hidden states of those systems. These solutions might involve complex algorithms and also presume higher computation power for achieving usable results in the prognosis of a system's future states, as well as possible training, using simulated or collected data.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Smart Preventive Maintenance of Hybrid Networks and IoT Systems Using Software Sensing and Future State Prediction

Minea

Minea²,

Semenescu

2023

Sensors

View full text Add to dashboard Cite

At present, IoT and intelligent applications are developed on a large scale. However, these types of new applications require stable wireless connectivity with sensors, based on several standards of communication, such as ZigBee, LoRA, nRF, Bluetooth, or cellular (LTE, 5G, etc.). The continuous expansion of these networks and services also comes with the requirement of a stable level of service, which makes the task of maintenance operators more difficult. Therefore, in this research, an integrated solution for the management of preventive maintenance is proposed, employing software-defined sensing for hardware components, applications, and client satisfaction. A specific algorithm for monitoring the levels of services was developed, and an integrated instrument to assist the management of preventive maintenance was proposed, which are based on the network of future states prediction. A case study was also investigated for smart city applications to verify the expandability and flexibility of the approach. The purpose of this research is to improve the efficiency and response time of the preventive maintenance, helping to rapidly recover the required levels of service, thus increasing the resilience of complex systems.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Smart Preventive Maintenance of Hybrid Networks and IoT Systems Using Software Sensing and Future State Prediction

Minea

Minea²,

Semenescu

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In the context of HMMs, the transition between hidden states is governed by transition probabilities. These probabilities determine the likelihood of moving from one hidden state to another, providing a framework for understanding the dynamic nature of the system (Gámiz et al, 2023). The utilization of hidden states in HMMs allows for modelling scenarios where certain aspects of the system are not directly measurable, making it a valuable tool in various fields, including finance, speech recognition, and bioinformatics.…”

Section: Using Hidden Markov Modelsmentioning

confidence: 99%

Machine learning in toxicological sciences: opportunities for assessing drug toxicity

Tonoyan,

Siraki

2024

Front. Drug Discov.

View full text Add to dashboard Cite

Machine learning (ML) in toxicological sciences is growing exponentially, which presents unprecedented opportunities and brings up important considerations for using ML in this field. This review discusses supervised, unsupervised, and reinforcement learning and their applications to toxicology. The application of the scientific method is central to the development of a ML model. These steps involve defining the ML problem, constructing the dataset, transforming the data and feature selection, choosing and training a ML model, validation, and prediction. The need for rigorous models is becoming more of a requirement due to the vast number of chemicals and their interaction with biota. Large datasets make this task possible, though selecting databases with overlapping chemical spaces, amongst other things, is an important consideration. Predicting toxicity through machine learning can have significant societal impacts, including enhancements in assessing risks, determining clinical toxicities, evaluating carcinogenic properties, and detecting harmful side effects of medications. We provide a concise overview of the current state of this topic, focusing on the potential benefits and challenges related to the availability of extensive datasets, the methodologies for analyzing these datasets, and the ethical implications involved in applying such models.

show abstract

“…Following a similar strategy as in [37], we consider preventive maintenance criteria based on a critical state probability criterion (CSPC). Roughly speaking, a preventive maintenance action is carried out when the system enters a subset of operational states that are considered critical in some sense.…”

Section: Preventive Maintenance Based On the Critical State Probabili...mentioning

confidence: 99%

MoMA Algorithm: A Bottom-Up Modeling Procedure for a Modular System under Environmental Conditions

et al. 2022

Self Cite

View full text Add to dashboard Cite

The functioning of complex systems relies on subsystems (modules) that in turn are composed of multiple units. In this paper, we focus on modular systems that might fail due to wear on their units or environmental conditions (shocks). The lifetimes of the units follow a phase-type distribution, while shocks follow a Markovian Arrival Process. The use of Matrix-Analytic methods and a bottom-up approach for constructing the system generator is proposed. The use of modular structures, as well as its implementation by the Modular Matrix-Analytic (MoMA) algorithm, make our methodology flexible in adapting to physical changes in the system, e.g., incorporation of new modules into the current model. After the model for the system is built, the modules are seen as a ‘black box’, i.e., only the contribution of the module as a whole to system performance is considered. However, if required, our method is able to keep track of the events within the module, making it possible to identify the state of individual units. Compact expressions for different reliability measures are obtained with the proposed description, optimal maintenance strategies based on critical operative states are suggested, and a numerical application based on a k-out-of-n structure is developed.

show abstract

Hidden markov models in reliability and maintenance

Cited by 30 publications

References 26 publications

Smart Preventive Maintenance of Hybrid Networks and IoT Systems Using Software Sensing and Future State Prediction

Smart Preventive Maintenance of Hybrid Networks and IoT Systems Using Software Sensing and Future State Prediction

Machine learning in toxicological sciences: opportunities for assessing drug toxicity

MoMA Algorithm: A Bottom-Up Modeling Procedure for a Modular System under Environmental Conditions

Contact Info

Product

Resources

About