相关论文: Degradation Detection Method for Railway Point Mac…

Monitoring wastewater pump stations is essential because they are critical infrastructure. However, monitoring is still often performed manually due to the lack of suitable algorithmic methods and data. This paper introduces a…

Convergence detection of iterative stochastic optimization methods is of great practical interest. This paper considers stochastic gradient descent (SGD) with a constant learning rate and momentum. We show that there exists a transient…

Ensuring the resilience of computer-based railways is increasingly crucial to account for uncertainties and changes due to the growing complexity and criticality of those systems. Although their software relies on strict verification and…

We consider the problem of identifying a single line outage in a power grid by using data from phasor measurement units (PMUs). When a line outage occurs, the voltage phasor of each bus node changes in response to the change in network…

Milling machines form an integral part of many industrial processing chains. As a consequence, several machine learning based approaches for tool wear detection have been proposed in recent years, yet these methods mostly deal with standard…

We present a long-term deployment study of a machine vision-based anomaly detection system for failure prediction in a steel rolling mill. The system integrates industrial cameras to monitor equipment operation, alignment, and hot bar…

Context: Performance regressions negatively impact execution time and memory usage of software systems. Nevertheless, there is a lack of systematic methods to evaluate the effectiveness of performance test suites. Performance mutation…

Change-point detection methods are proposed for the case of temporary failures, or transient changes, when an unexpected disorder is ultimately followed by a readjustment and return to the initial state. A base distribution of the…

In the domain of rotating machinery, bearings are vulnerable to different mechanical faults, including ball, inner, and outer race faults. Various techniques can be used in condition-based monitoring, from classical signal analysis to deep…

The detection of masonry damage is essential for preventing potentially disastrous outcomes. Manual inspection can, however, take a long time and be hazardous to human inspectors. Automation of the inspection process using novel computer…

Software defect prediction is an essential task during the software development Lifecycle as it can help managers to identify the most defect-proneness modules. Thus, it can reduce the test cost and assign testing resources efficiently.…

Vibration measurements have been used to reliably diagnose performance problems in machinery and related mechanical products. A vibration data collector can be used effectively to measure and analyze the machinery vibration content in…

Deep learning-based object detection is a powerful approach for detecting faulty insulators in power lines. This involves training an object detection model from scratch, or fine tuning a model that is pre-trained on benchmark computer…

Predictive maintenance in manufacturing industry applications is a challenging research field. Packaging machines are widely used in a large number of logistic companies' warehouses and must be working uninterruptedly. Traditionally,…

Many modern applications require detecting change points in complex sequential data. Most existing methods for change point detection are unsupervised and, as a consequence, lack any information regarding what kind of changes we want to…

With the growing number of severe system disturbances and blackouts around the world, controlled system separation is becoming an increasingly important system integrity protection scheme (SIPS) to save the electric power system from a…

The validation of data from sensors has become an important issue in the operation and control of modern industrial plants. One approach is to use knowledge based techniques to detect inconsistencies in measured data. This article presents…

Distribution system state estimation (DSSE) is an essential tool for operation of distribution networks, the results of which enables the operator to have a thorough observation of the system. Thus, most distribution management systems…

Within smart manufacturing, data driven techniques are commonly adopted for condition monitoring and fault diagnosis of rotating machinery. Classical approaches use supervised learning where a classifier is trained on labeled data to…

This research proposes a Ground Penetrating Radar (GPR) data processing method for non-destructive detection of tunnel lining internal defects, called defect segmentation. To perform this critical step of automatic tunnel lining detection,…