Fault detection on robot manipulators using artificial neural networks

Eski, İkbal; Erkaya, Selçuk; Savaş, Sertaç; Yıldırım, Şahin

doi:10.1016/j.rcim.2010.06.017

Cited by 90 publications

(38 citation statements)

References 16 publications

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“…In Brambilla et al (2008) and De Luca and Mattone (2004), nonlinear observers are used together with adaptive schemes while in Caccavale et al (2009), the authors mix the use of nonlinear observers with support vector machines. The problem has also been approached by the use of neural networks as presented in Vemuri and Polycarpou (2004) and in Eski et al (2010), where vibration data are used for diagnosis. Parameter estimation is a natural approach because it can use the physical interpretation of the system, see for example Freyermuth (1991).…”

Section: Introductionmentioning

confidence: 99%

Modeling and Identification of Wear in a Robot Joint under Temperature Uncertainties

Bittencourt

Axelsson

Jung

et al. 2011

IFAC Proceedings Volumes

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This paper considers the problem of wear estimation in a standard industrial robot joint. Motivated by the observation that the wear processes in a robot actuator cause an increase of the friction levels in the joint, static friction observations are used to infer the wear levels. An existing static friction model is extended to include the eects of wear, which are analyzed and modeled. The resulting model can explain friction in a robot joint with respect to changes in speed, load, temperature and wear. Based on this model and a test-cycle that generates static friction observations, a modelbased wear estimator is proposed. The performance of the estimator is found both by means of simulations and experiments on an industrial robot. As it is shown, the method can distinguish the eects of wear even under large temperature variations, opening up for the use of robust joint diagnosis for industrial robots. Abstract: This paper considers the problem of wear estimation in a standard industrial robot joint. Motivated by the observation that the wear processes in a robot actuator cause an increase of the friction levels in the joint, static friction observations are used to infer the wear levels.An existing static friction model is extended to include the effects of wear, which are analyzed and modeled. The resulting model can explain friction in a robot joint with respect to changes in speed, load, temperature and wear. Based on this model and a test-cycle that generates static friction observations, a model-based wear estimator is proposed. The performance of the estimator is found both by means of simulations and experiments on an industrial robot. As it is shown, the method can distinguish the effects of wear even under large temperature variations, opening up for the use of robust joint diagnosis for industrial robots.

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Section: Introductionmentioning

confidence: 99%

Modeling and Identification of Wear in a Robot Joint under Temperature Uncertainties

Bittencourt

Axelsson

Jung

et al. 2011

IFAC Proceedings Volumes

View full text Add to dashboard Cite

show abstract

“…After normalization, inputs and outputs of the model are converted tõm ax ,̃m ax ,̃m ax , and̃,̃. The latent models can be represented using = (̃m ax ,̃m ax ,̃m ax ) = (̃m ax ,̃m ax ,̃m ax ) (21) where , are the underlying functions.…”

Section: Maximum Accelerationmentioning

confidence: 99%

“…A lot of model and signal based methods have been proposed to solve the fault detection problem for machines [18][19][20] and robots [21,22]. In this paper, the vibration signals are used to find the optimal dynamic motion parameters.…”

Section: Introductionmentioning

confidence: 99%

Reducing the Frame Vibration of Delta Robot in Pick and Place Application: An Acceleration Profile Optimization Approach

Cheng

Wei

2018

Shock and Vibration

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Delta robot is typically mounted on a frame and performs high speed pick and place tasks from top to bottom. Because of its outstanding accelerating capability and higher center of mass, the Delta robot can generate significant frame vibration. Existing trajectory smoothing methods mainly focus on vibration reduction for the robot instead of the frame, and modifying the frame structure increases the manufacturing cost. In this paper, an acceleration profile optimization approach is proposed to reduce the Delta robot-frame vibration. The profile is determined by the maximum jerk, acceleration, and velocity. The pick and place motion (PPM) and resulting frame vibration are analyzed in frequency domain. Quantitative analysis shows that frame vibration can be reduced by altering those dynamic motion parameters. Because the analytic model is derived based on several simplifications, it cannot be directly applied. A surrogate model-based optimization method is proposed to solve the practical issues. By directly executing the PPM with different parameters and measuring the vibration, a model is derived using Gaussian Process Regression (GPR). In order to reduce the frame vibration without sacrificing robot efficiency, those two goals are fused together according to their priorities. Based on the surrogate model, a single objective optimization problem is formulated and solved by Genetic Algorithm (GA). Experimental results show effectiveness of the proposed method. Behavior of the optimal parameters also verifies the robot-frame vibration mechanism.

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“…In recent years, the signal processing based FDI research has been actively conducted. Fault detection by neural network is a typical method among signal processing based methods [9], [10]. The NN is trained by using measured values with failure information to learn the relationship between the measured values and the failure.…”

Section: Fault Detector In Control Systemsmentioning

confidence: 99%

Recurrent neural network-based fault detector for aileron failures of aircraft

Yoshikawa

Belkhir

Suzuki

2017

2017 11th Asian Control Conference (ASCC)

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Abstract-This paper empirically investigate the design of a fault detection mechanism based on Long Short Term Memory (LSTM) neural network. Given an equation based model that approximate the behavior of aircraft ailerons, the fault detector aims at predicting the state of aircraft: the normal state for which no failure are observed, or four different failure states, e.g. a delay changes. This is achieved by collecting a limited amount of command and responses data by varying the parameters of the aileron model, such that a LSTM network is used to predict the state of the aircraft of sequence of the pair commands/responses. In this empirical study we empirically demonstrated LSTM networks can be a promising approach for fault detection, and achieve reasonable performances despite a limited amount of data, in particular avoiding overfitting of the model.

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Fault detection on robot manipulators using artificial neural networks

Cited by 90 publications

References 16 publications

Modeling and Identification of Wear in a Robot Joint under Temperature Uncertainties

Modeling and Identification of Wear in a Robot Joint under Temperature Uncertainties

Reducing the Frame Vibration of Delta Robot in Pick and Place Application: An Acceleration Profile Optimization Approach

Recurrent neural network-based fault detector for aileron failures of aircraft

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