Assessing the effectiveness of data-driven time-domain condition indicators in predicting the progression of surface distress under rolling contact

Condition monitoring of machine health via analysis of vibration, acoustic and other signals offers an important tool for reducing the machine downtime and maintenance costs. The key aspect in this process is the ability to relate features derived from the recorded sensor signals to the physical condition of the monitored asset in real time. This paper uses simple machine learning techniques to examine the ability of specific time-domain features obtained from vibration signals to predict the progression of surface distress in lubricated, rolling-sliding contacts, such as those found in rolling bearings and gears. Controlled experiments were performed on a triple-disc rolling contact fatigue rig using seeded-fault roller specimens where micropitting damage was generated and its progression directly observed over millions of contact cycles. Vibration signals were recorded throughout the experiments. Features known as condition indicators were then extracted from the recorded time-domain signals and their evolution related to the observed physical state of the associated specimens using simple machine learning techniques. Five time-domain condition indicators were examined, peak-to-peak, root-mean-square, kurtosis, crest factor and skewness, three of which were found not to be redundant. First, a classification model using KNN nearest neighbor was built with the three informative condition indicators as training data. The cross-validation results indicated that this classifier was able to predict the presence of micropitting damage with a relatively high precision and a low rate of false positives. Secondly, a k-means clustering analysis was performed to measure the significance of each condition indicator by leveraging patterns. The peak-to-peak condition indicator was found to be a good predictor for progression of micropitting damage. In addition, this indicator was able to distinguish between micropitting and pitting failure modes with a high success rate. Finally, the condition indicator response was correlated with the predicted damage state of the test specimen obtained through an existing physics-based surface distress model in order to illustrate the potential of hybrid models for improved prognostics of damage progression in rolling-sliding tribological contacts.

THE THEORY OF EXPLOITATION AS THE UNEQUAL EXCHANGE OF LABOUR

This paper explores the foundations of the theory of exploitation as the unequal exchange of labour (UEL). The key intuitions behind all of the main approaches to UEL exploitation are explicitly analysed as a series of formal axioms in a general economic environment. Then, a single domain condition calledLabour Exploitationis formulated, which summarizes the foundations of UEL exploitation theory, defines the basic domain of all UEL exploitation forms, and identifies the formal and theoretical framework for the analysis of the appropriate definition of exploitation.

GeneralizedS-procedure and finite frequency KYP lemma

The contribution of this paper is twofold. First we give a generalization of theS-procedure which has been proven useful for robustness analysis of control systems. We then apply the generalizedS-procedure to derive an extension of the Kalman – Yakubovich – Popov lemma that converts a frequency domain condition within a finite interval to a linear matrix inequality condition suitable for numerical computations.