Condition assessment for automatic tool changer based on sparsity-enabled signal decomposition method

Chen, Xuefeng; Wei, Xin

doi:10.1016/j.mechatronics.2014.12.006

Cited by 29 publications

(10 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this approach, an algorithm called greedy Bayesian method is proposed to efficiently obtain the sparse estimation from heterogeneous sensor data. Chen et al [39] deployed sparsity-enabled signal decomposition method to establish a condition monitoring system for Automatic tool changer of CNC (Computer numerical control) machine. In this approach, morphological component analysis is used to construct the objective function for signal decomposition, which is further used to obtain the sparse representation.…”

Section: Data Sparsity and Censoring In Pdmmentioning

confidence: 99%

Predictive maintenance using cox proportional hazard deep learning

Chen

Liu

Wang

et al. 2020

Advanced Engineering Informatics

View full text Add to dashboard Cite

Predictive maintenance (PdM) has become prevalent in the industry in order to reduce maintenance cost and to achieve sustainable operational management. The core of PdM is to predict the next failure so corresponding maintenance can be scheduled before it happens. The purpose of this study is to establish a Time-Between-Failure (TBF) prediction model through a data-driven approach. For PdM, data sparsity is regarded as a critical issue which can jeopardize algorithm performance for the modelling based on maintenance data. Meanwhile, data censoring has imposed another challenge for handling maintenance data because the censored data is only partially labelled. Furthermore, data sparsity may hurt algorithm performance of the existing approaches when addressing the data censoring issue. In this study, a new approach called Cox proportional hazard deep learning (CoxPHDL) is proposed to tackle the aforementioned issues of data sparsity and data censoring that are common in the analysis of operational maintenance data. The idea is to offer an integrated solution by taking advantage of deep learning and reliability analysis. To start with, an autoencoder is adopted to convert the nominal data into a robust representation. Secondly, a Cox PHM is researched to estimate the TBF of the censored data. A long-short-term memory (LSTM) network is then established to train the TBF prediction model based on the pre-processed maintenance data. Experimental studies using a sizable real-world fleet maintenance data set provided by a UK fleet company have demonstrated the merits of the proposed approach where the algorithm performance based on the proposed LSTM network has been improved respectively in terms of MCC and RMSE.

show abstract

Section: Data Sparsity and Censoring In Pdmmentioning

confidence: 99%

Predictive maintenance using cox proportional hazard deep learning

Chen

Liu

Wang

et al. 2020

Advanced Engineering Informatics

View full text Add to dashboard Cite

show abstract

“…Accurate fault identification from the low-resonance component verified RSSD’s validity. In addition, a condition assessment system for an automatic tool changer in CNC machining centers was established by Chen [ 44 ]. This monitoring system, based on RSSD, could not only detect the transient component caused by a globoidal indexing cam fault, but also locate the fault’s exact position.…”

Section: Applications Of Rssd In Mechanical Fault Diagnosismentioning

confidence: 99%

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review

Huang

Sun

Wang

2017

Sensors

View full text Add to dashboard Cite

Mechanical equipment is the heart of industry. For this reason, mechanical fault diagnosis has drawn considerable attention. In terms of the rich information hidden in fault vibration signals, the processing and analysis techniques of vibration signals have become a crucial research issue in the field of mechanical fault diagnosis. Based on the theory of sparse decomposition, Selesnick proposed a novel nonlinear signal processing method: resonance-based sparse signal decomposition (RSSD). Since being put forward, RSSD has become widely recognized, and many RSSD-based methods have been developed to guide mechanical fault diagnosis. This paper attempts to summarize and review the theoretical developments and application advances of RSSD in mechanical fault diagnosis, and to provide a more comprehensive reference for those interested in RSSD and mechanical fault diagnosis. Followed by a brief introduction of RSSD’s theoretical foundation, based on different optimization directions, applications of RSSD in mechanical fault diagnosis are categorized into five aspects: original RSSD, parameter optimized RSSD, subband optimized RSSD, integrated optimized RSSD, and RSSD combined with other methods. On this basis, outstanding issues in current RSSD study are also pointed out, as well as corresponding instructional solutions. We hope this review will provide an insightful reference for researchers and readers who are interested in RSSD and mechanical fault diagnosis.

show abstract

“…Number of tools and tool select time should be taken into account while design magazine [6,7]. Generally tool holder located on disc is asked to keep equal spacing except for different sizes of tool diameter.…”

Section: Magazine Designmentioning

confidence: 99%

Design and Control of Automatic Tool Changer for Machine Center

Niu¹

2017

dtetr

View full text Add to dashboard Cite

Abstract. Automatic tool changer (ATC) device is an essential part in CNC Machining centers, which can make the machining centers smoothly running using the tool magazine and automatic tool changer control system. In order to reduce the non-cutting during work, improve productivity and reduce production costs, machinery part and control system of ATC are designed based on the disk tool magazine and tool change process of machinery arm. In this system, PLC is used as brain to magazine the tool moving, automatic tool selection, cutter spindle sleeve rollover and exchanging control. Results show that the designed automatic tool changer device and control system have the characteristic of stable performance, precise control, simple operation, and convenient monitoring. From the view of industrial applications, this system has well practical value.

show abstract

Condition assessment for automatic tool changer based on sparsity-enabled signal decomposition method

Cited by 29 publications

References 34 publications

Predictive maintenance using cox proportional hazard deep learning

Predictive maintenance using cox proportional hazard deep learning

Resonance-Based Sparse Signal Decomposition and its Application in Mechanical Fault Diagnosis: A Review

Design and Control of Automatic Tool Changer for Machine Center

Contact Info

Product

Resources

About