Tool wear detection in turning operations using singular spectrum analysis

Salgado, David Rodríguez; Sánchez, Francisco Javier Alonso

doi:10.1016/j.jmatprotec.2005.08.005

Cited by 91 publications

(51 citation statements)

References 28 publications

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“…Recently, it was used for the purposes of engineering application such as fault diagnosis of rolling element bearings [15][16][17][18][19], tool wear health monitoring [20,21] and delamination in composite materials [22].…”

Section: Methodsmentioning

confidence: 99%

Vibration-based damage detection of structural joints in presence of uncertainty

2018

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Abstract. Early damage detection of structure's joints is essential in order to ensure the integrity of structures. Vibration-based methods are the most popular way of diagnosing damage in machinery joints. Any technique that is used for such a purpose requires dealing with the variability inherent to the system due to manufacturing tolerances, environmental conditions or aging. The level of variability in vibrational response can be very high for mass-produced complex structures that possess a large number of components. In this study, a simple and efficient time frequency method is proposed for detection of damage in connecting joints. The method suggests using singular spectrum analysis for building a reference space from the signals measured on a healthy structure and then compares all other signals to that reference space in order to detect the presence of faults. A model of two plates connected by a series of mounts is used to examine the effectiveness of the method where the uncertainty in the mount properties is taken into account to model the variability in the built-up structure. The motivation behind the simplified model is to identify the faulty mounts in trim-structure joints of an automotive vehicle where a large number of simple plastic clips are used to connect the trims to the vehicle structure.

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Section: Methodsmentioning

confidence: 99%

Vibration-based damage detection of structural joints in presence of uncertainty

2018

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“…Fundamentally, the set of series obtained from the decomposition can be interpreted as a slowly varying trend representing the signal mean at each instant, a set of periodic series, and aperiodic noise [24,25,26].…”

Section: Singular Spectrum Analysismentioning

confidence: 99%

“…By using SSA we get m subsets signed D i1 , D i2 , ..., D im , where m is a parameter selected by the designer. As mentioned in [11,25], the original mechanical vibration is prone to the low frequency range. Therefore, by putting the eigenvalues in the decreasing order, among the m subsets we delete (m − k) last subsets which are considered as noise.…”

Section: Building the Idsmentioning

confidence: 99%

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A Method of Bearing Fault Diagnosis Using Singular Spectrum Analysis, Sparse Filtering and Anfis

Nguyen¹,

Nguyen

2018

JCC

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Abstract. Bearing is an important machine detail participating in almost all mechanical systems.Estimating online its operating condition to exploit actively the systems, therefore, is one of the most urgent requirements. This paper presents an online bearing damage identifying method named ASBDIM based on ANFIS (Adaptive Neuro-Fuzzy Inference System), Singular Spectrum Analysis (SSA) and sparse filtering. This is an online estimating process operated via two phases, offline and online one. In the offline period, by using SSA and sparse filtering, a database signed Off DaB is built whose inputs are features extracted from the measured data stream typed big data, while its outputs are values encoding the surveyed bearing damage statuses. The ANFIS is then employed to identify the dynamic response of the mechanical system corresponding to the bearing damage statuses reflected by the Off DaB. In the online period, first, at each estimating time, another database called On DaB is established using the way similar to the one used for building the Off DaB. The On DaB participates as inputs of the ANFIS to generate its outputs which are then compared with the corresponding encoded outputs to specify bearing real status at this time. Survey results based on different data sources showed the effectiveness of the proposed method.Keywords. Identifying bearing damage, AI for estimating damage, ANFIS based damage identification, SSA for identifying damage.

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“…It is also used as an anomaly detection method in tool wear health monitoring [19,20] and for damage assessment in wind turbine blades [21] , but it is still unpopular for fault detection in rolling element bearings. SSA has the capability to distinguish between different data categories when such are present in the data analysed [22][23][24] .…”

Section: Singular Spectrum Analysis | Featurementioning

confidence: 99%

A new methodology for fault detection in rolling element bearings using singular spectrum analysis

Al-Bugharbee¹,

Trendafilova²

2017

int j cond monitor

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Tool wear detection in turning operations using singular spectrum analysis

Cited by 91 publications

References 28 publications

Vibration-based damage detection of structural joints in presence of uncertainty

Vibration-based damage detection of structural joints in presence of uncertainty

A Method of Bearing Fault Diagnosis Using Singular Spectrum Analysis, Sparse Filtering and Anfis

A new methodology for fault detection in rolling element bearings using singular spectrum analysis

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