Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing

Motahari-Nezhad, Mohsen; Jafari, Seyed Mohammad

doi:10.1016/j.eswa.2020.114391

Cited by 66 publications

(17 citation statements)

References 33 publications

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“…For these reasons, among the various component of machine, bearing remaining useful life prediction is used in the main predictive maintenance method. Bearing remaining useful life prediction is mainly analyzed via vibration signals and sound [7], and various signal processing technologies such as noise reduction and fault frequency measurement are used.…”

Section: Prognostics and Health Managementmentioning

confidence: 99%

A One-Stage Ensemble Framework based on Convolutional Autoencoder for Remaining Useful Life Estimation

Park

Kim

2021

Preprint

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The research on predictive maintenance of rotating machines, the most important element in manufacturing facilities, has been very active. The widespread availability of smart factory solutions has led to improved data collection from machines and processes and is able to provide key information. For our purpose, the collected information enables the maintenance system to predict the remaining useful life using deep learning models. The introduction of multi-layer perceptron of signal processing originating from bearings, in time series data, has been discussed in many publications. However, estimating accuracy for the remaining useful life is determined by the selection of the feature domain and the concatenation network model. Herein, we introduce a convolutional Autoencoder based on multi-domain ensemble learning in order to include various feature domains and a concatenation network operated by latent space into a single neural network. The performance of the proposed model is evaluated by using a simple health indicator and a PRONOSTIA dataset and compared with a simple concatenation model, 2-stage Autoencoder, and a recurrent neural network.

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Section: Prognostics and Health Managementmentioning

confidence: 99%

A One-Stage Ensemble Framework based on Convolutional Autoencoder for Remaining Useful Life Estimation

Park

Kim

2021

Preprint

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“…The authors of [1] collect a series of works carried out from 2015 to 2020, based on machine learning techniques to facilitate predictive maintenance in manufacturing contexts. For example, both [2,3] propose a data-driven bearing performance degradation assessment method, monitoring bearing running states to ensure machine safety; however, in this paper, we deviate from a purely manufacturing context, seeking to exploit the benefits that IoT has brought to connected vehicles. In connected vehicles, the term Internet of Vehicles (IoV) is used [4,5], referring to the situation in which data can be locally collected on the vehicle and then sent to a remote storage location (cloud) where they can be analyzed in more detail.…”

Section: Literature Reviewmentioning

confidence: 99%

Empowering Commercial Vehicles through Data-Driven Methodologies

et al. 2021

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In the era of “connected vehicles,” i.e., vehicles that generate long data streams during their usage through the telematics on-board device, data-driven methodologies assume a crucial role in creating valuable insights to support the decision-making process effectively. Predictive analytics allows anticipation of vehicle issues and optimized maintenance, reducing the resulting costs. In this paper, we focus on analyzing data collected from heavy trucks during their use, a relevant task for companies due to the high commercial value of the monitored vehicle. The proposed methodology, named TETRAPAC, offers a generalizable approach to estimate vehicle health conditions based on monitored features enriched by innovative key performance indicators. We discussed performance of TETRAPAC in two real-life settings related to trucks. The obtained results in both tasks are promising and able to support the company’s decision-making process in the planning of maintenance interventions.

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“…Vibration analysis [7][8][9] and motor current signature analysis (MCSA) [10] [11] are two of the most popular research in this field. Stator current analysis is known for providing non-invasive condition monitoring for EV motors [12][13][14]. Accurate detection of potential or existing motor faults is an essential measure to maintain safe machine operation.…”

Section: Introduction Ault Detection and Diagnosis (Fdd) Is A Conditionmentioning

confidence: 99%

Artificial Intelligence-Based Technique for Fault Detection and Diagnosis of EV Motors: A Review

Lang¹,

Gong

et al. 2022

IEEE Trans. Transp. Electrific.

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The motor drive system plays a significant role in the safety of electric vehicles as a bridge for power transmission. Meanwhile, to enhance the efficiency and stability of the drive system, more and more studies based on AI technology are devoted to the fault detection and diagnosis of the motor drive system. This paper reviews the application of AI techniques in motor fault detection and diagnosis in recent years. AI-based FDD is divided into two main steps: feature extraction and fault classification. The application of different signal processing methods in feature extraction is discussed. In particular, the application of traditional machine learning and deep learning algorithms for fault classification is presented in detail. In addition, the characteristics of all techniques reviewed are summarized. Finally, the latest developments, research gaps and future challenges in fault monitoring and diagnosis of motor faults are discussed.

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Bearing remaining useful life prediction under starved lubricating condition using time domain acoustic emission signal processing

Cited by 66 publications

References 33 publications

A One-Stage Ensemble Framework based on Convolutional Autoencoder for Remaining Useful Life Estimation

A One-Stage Ensemble Framework based on Convolutional Autoencoder for Remaining Useful Life Estimation

Empowering Commercial Vehicles through Data-Driven Methodologies

Artificial Intelligence-Based Technique for Fault Detection and Diagnosis of EV Motors: A Review

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