The Analysis of Time Series: An Introduction, 4th edn. By C. Chatfield. ISBN 0 412 31820 2. Chapman and Hall, London, 1989. 242 pp. £13.50.

The Analysis of Time Series: An Introduction

With growing dependence of industrial robots, a failure of an industrial robot may interrupt current operation or even overall manufacturing workflows in the entire production line, which can cause significant economic losses. Hence, it is very essential to maintain industrial robots to ensure high-level performance. It is widely desired to have a real-time technique to constantly monitor robots by collecting time series data from robots, which can automatically detect incipient failures before robots totally shut down. Model-based methods are typically used in anomaly detection for robots, yet explicit domain knowledge and accurate mathematical models are required. Data-driven techniques can overcome these limitations. However, a major difficulty for them is the lack of sufficient fault data of industrial robots. Besides, the used technique for anomaly detection of robots should be required to not only capture the temporal dependency in collected time series data, but also the inter-correlations between different metrics. In this paper, we introduce an unsupervised anomaly detection for industrial robots, sliding-window convolutional variational autoencoder (SWCVAE), which can realize real-time anomaly detection spatially and temporally by coping with multivariate time series data. This method has been verified by a KUKA KR6R 900SIXX industrial robot, and the results prove that the proposed model can successfully detect anomaly in the robot. Thus, this work presents a promising tool for condition-based maintenance of industrial robots.

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Unsupervised Anomaly Detection of Industrial Robots Using Sliding-Window Convolutional Variational Autoencoder

The predictive maintenance of industrial machines is one of the challenging applications in the new era of Industry 4.0. Thanks to the predictive capabilities offered by the emerging smart data analytics, data-driven approaches for condition monitoring are becoming widely used for early detection of anomalies on production machines. The aim of this paper is to provide insights on the predictive maintenance of industrial robots and the possibility of building a condition-monitoring system based on the data analysis of robot's power measurements. A predictive modeling approach is proposed to detect robot manipulator accuracy errors based on robot's current data analysis for predictive maintenance purposes. An experimental procedure is also carried out to oversee the correlation between the robot accuracy error and a set of extracted features from current time-series, and to evaluate the proposed predictive modeling. The obtained results are satisfactory and prove the feasibility of building a data-driven condition monitoring of robot manipulators using the electrical power time-series data analysis.

Data analytics for predictive maintenance of industrial robots

We have applied and evaluated system identification methods using both commercial software and dedicated subspace model identification software (MOESP). Results using the different software tools have been similar (but not identical) in accuracy and predictive power, the main differences being the time required for computation and occasional failures of one algorithm in delivery of a stable model. For linear model identification all methods tested failed to provide residuals, i.e. model misfit, uncorrelated with input and without significant autocorrelation. As a result, no linear stochastic innovations model could be formulated in any satisfactory manner. However, model-order tests based on singular values suggest that a low model order be sufficient for input–output modeling to within a modeling accuracy of 2–5%. Thus, the identification of a state-space model combined with a friction model provides effective means to modeling in robotics.

State-space system identification of robot manipulator dynamics

Gearbox failures cost thousands of lost production hours in plants that use industrial robots. In this context, an automated monitoring system that can warn the user of an impending failure can save precious resources. This problem has been addressed in many other domains through the use of machine learning approaches. However, standard machine learning algorithms are limited in their ability to detect gearbox failures, mainly due to task variability arises from robot-specific data. To improve detection performance of machine learning approaches, in this paper we propose techniques to curate the data prior to building a classification model. In a systematic hypothesis–driven study exploring the effect of different preprocessing techniques, we evaluate training data augmentation with estimated measurements, data differencing to suppress task dependence, inclusion of local variation, and selection of principal components on data collected from 26 industrial robots from the field. Our results show that preprocessing techniques improve the failure detection performance.

Data-Driven Gearbox Failure Detection in Industrial Robots

Training data selection criteria for detecting failures in industrial robots

In this paper we simulate, detect, and locate wear in industrial robots operating over long time-periods on repetitive tasks. We simulate wear using the MATLAB robotics toolbox by increasing the Coulomb friction coefficient on select robot axes. Disturbance and measurement noise are included in each axis while computing the inverse and forward kinematics. To compare change across all configurations within a repetitive cycle, the output torque data is transformed into a normalized distribution sampled once every hour. We identify wear as significant change in torque values from normal operating conditions, and detect wear using principal component analysis; the source axis is located by cross-correlating the output data from all axes through time. Simulation results with a range of friction values tested for three different tasks across all axes show that wear detection on the basis of comparison with normal operating condition depends on the manipulator task, whereby onset of wear was detected in five out of six axes for one task and two out of six axes in the rest. Wear location was correctly identified in all successful detections except one.

A simulation based approach to detect wear in industrial robots

As industrial systems are getting complicated, challenges in coming up with efficient maintenance strategies which include predicting failures in the system become important industry specific research topic. Traditionally, research focuses on developing failure prediction models based on physical understanding of the system. But, development of such models are often time consuming and labour intensive for complex systems. Inrecent past, due to advent of cheaper data collection mechanisms and efficient algorithms, data driven approaches for predicting failures are gaining significant interest in industrial research community. In this paper, we provide a Principal component Analysis (PCA) based approach of failure prediction in industrial robots using event log information. The event logs are collected through remote service set-up from a robot controller. The proposed method will reduce the dimensionality of the original data which consist of interrelated events while retaining the variation present in the data. Using PCA and multivariate statistics such as Hotelling T2, Q Residuals and Q contributions charts, we are able to detect abnormal behavior of event pattern within 30 days before failure.

Event Based Robot Prognostics Using Principal Component Analysis

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Training data selection criteria for detecting failures in industrial robots

A simulation based approach to detect wear in industrial robots

Event Based Robot Prognostics Using Principal Component Analysis