Tool wear monitoring by machine learning techniques and singular spectrum analysis

Kilundu, Bovic; Dehombreux, Pierre; Chiementin, Xavier

doi:10.1016/j.ymssp.2010.07.014

Cited by 110 publications

(51 citation statements)

References 24 publications

(27 reference statements)

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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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“…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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“…Eddy currents Target (conductive material) In order to overcome the disadvantages of commercial dynamometers, many researchers have used the power sensor to estimate cutting force in cutting process as presented in references [7][8][9][10]. In summary, the power (current) sensor is less expensive, more durable and flexible, and also very simple to install.…”

Section: Alternating Magnetic Fieldmentioning

confidence: 99%

A comparative study of using spindle motor power and eddy current for the detection of tool conditions in milling processes

Abbass¹,

Al‐Habaibeh

2015

2015 IEEE 13th International Conference on Industrial Informatics (INDIN)

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Abstract-This paper investigates the use of the power of the driving motor of a CNC spindle in comparison to two perpendicular eddy current sensors for the detection of tool wear in milling processes. Monitoring the power through the current profile is a low cost system which has been utilised in this study as an attempt to detect the fluctuation in the motor load as a result of the conditions of the cutting tool. Eddy current sensors are dedicated sensors that are installed on the spindle to measure the vibration of the rotating spindle in two axes. Experimental work has been conducted using fresh and worn tools to investigate the effect of tool conditions on the two sensory systems. Time domain features are utilised to compare between the two sensors in relation to this application. The results indicate that Eddy current sensors are found to be more successful and sensitive, in general, than the power of the motor in detecting the changes of the cutting tools during the machining operation. However, the kurtosis value of the power of the spindle has been found to be successful in predicting the tool conditions with high sensitivity.

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Tool wear monitoring by machine learning techniques and singular spectrum analysis

Cited by 110 publications

References 24 publications

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

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

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

A comparative study of using spindle motor power and eddy current for the detection of tool conditions in milling processes

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