Condition monitoring

About: Condition monitoring is a research topic. Over the lifetime, 13911 publications have been published within this topic receiving 201649 citations.

An on-line stator winding resistance estimation technique for temperature monitoring of line-connected induction machines

Abstract: A new technique for stator resistance (R/sub s/)-based thermal monitoring of small line-connected induction machines is proposed in this paper. A simple device is developed for injecting a small DC signal into line-connected induction machines for estimation of R/sub s/. The proposed DC injection device is capable of intermittently injecting a controllable DC bias into the motor with very low power dissipation. Experimental results under motor startup, load variation, and abnormal cooling conditions verify that the proposed technique provides an accurate estimate of R/sub s/ that is capable of responding to the changes in the motor thermal characteristics, resulting in reliable thermal protection. The proposed technique is a very practical method for thermal protection of small line-connected induction machines that can be implemented with low cost in a motor condition monitoring system.

Monitoring wind turbine gearboxes

Abstract: Concerns amongst wind turbine (WT) operators about gearbox reliability arise from complex repair procedures, high replacement costs and long downtimes leading to revenue losses. Therefore, reliable monitoring for the detection, diagnosis and prediction of such faults are of great concerns to the wind industry. Monitoring of WT gearboxes has gained importance as WTs become larger and move to more inaccessible locations. This paper summarizes typical WT gearbox failure modes and reviews supervisory control and data acquisition (SCADA) and condition monitoring system (CMS) approaches for monitoring them. It then presents two up-to-date monitoring case studies, from different manufacturers and types of WT, using SCADA and CMS signals. The first case study, applied to SCADA data, starts from basic laws of physics applied to the gearbox to derive robust relationships between temperature, efficiency, rotational speed and power output. The case study then applies an analysis, based on these simple principles, to working WTs using SCADA oil temperature rises to predict gearbox failure. The second case study focuses on CMS data and derives diagnostic information from gearbox vibration amplitudes and oil debris particle counts against energy production from working WTs. The results from the two case studies show how detection, diagnosis and prediction of incipient gearbox failures can be carried out using SCADA and CMS signals for monitoring although each technique has its particular strengths. It is proposed that in the future, the wind industry should consider integrating WT SCADA and CMS data to detect, diagnose and predict gearbox failures.

Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment

Abstract: In the Internet-of-Things environment, it is critical to bridge the gap between business decision-making and real-time factory data to let companies transfer from condition-based maintenance service to predictive maintenance service. Condition monitoring systems have been widely applied to many industries to acquire operation and equipment related data, through which machine health state can be evaluated. One of the challenges of predicting future machine health lies in extracting the right features that are correlated well with the fault progression/degradation. We propose an enhanced restricted Boltzmann machine with a novel regularization term to automatically generate features that are suitable for remaining useful life prediction. The regularization term tries to maximize the trendability of the output features, which potentially better represent the degradation pattern of a system. The proposed method is benchmarked with regular restricted Boltzmann machine algorithm and principal component analysis. The generated features are used as input to a similarity-based method for life prediction. Run-to-failure datasets collected from two rotating systems are used for validation.

A Privacy-Preserving Vehicular Crowdsensing-Based Road Surface Condition Monitoring System Using Fog Computing

Abstract: In the recent past, great attention has been directed toward road surface condition monitoring. As a matter of fact, this activity is of critical importance in transportation infrastructure management. In response, multiple solutions have been proposed which make use of mobile sensing, more specifically contemporary applications and architectures that are used in both crowdsensing and vehicle-based sensing. This has allowed for automated control as well as analysis of road surface quality. These innovations have thus encouraged and showed the importance of cloud to provide reliable transport services to clients. Nonetheless, these initiatives have not been without challenges that range from mobility support, locational awareness, low latency, as well as geo-distribution. As a result, a new term has been coined for this novel paradigm, called, fog computing. In this paper, we propose a privacy-preserving protocol for enhancing security in vehicular crowdsensing-based road surface condition monitoring system using fog computing. At the onset, this paper proposes a certificateless aggregate signcryption scheme that is highly efficient. On the basis of the proposed scheme, a data transmission protocol for monitoring road surface conditions is designed with security aspects such as information confidentiality, mutual authenticity, integrity, privacy, as well as anonymity. In analyzing the system, the ability of the proposed protocol to achieve the set objectives and exercise higher efficiency with respect to computational and communication abilities in comparison to existing systems is also considered.

Novel Industrial Wireless Sensor Networks for Machine Condition Monitoring and Fault Diagnosis

Abstract: This paper proposes a novel industrial wireless sensor network (IWSN) for industrial machine condition monitoring and fault diagnosis. In this paper, the induction motor is taken as an example of monitored industrial equipment due to its wide use in industrial processes. Motor stator current and vibration signals are measured for further processing and analysis. On-sensor node feature extraction and on-sensor fault diagnosis using neural networks are then investigated to address the tension between the higher system requirements of IWSNs and the resource-constrained characteristics of sensor nodes. A two-step classifier fusion approach using Dempster-Shafer theory is also explored to increase diagnosis result quality. Four motor operating conditions-normal without load, normal with load, loose feet, and mass imbalance-are monitored to evaluate the proposed system. Experimental results show that, compared with raw data transmission, on-sensor fault diagnosis could reduce payload transmission data by 99%, decrease node energy consumption by 97%, and prolong node lifetime from 106 to 150 h, an increase of 43%. The final fault diagnosis results using the proposed classifier fusion approach give a result certainty of at least 97.5%. To leverage the advantages of on-sensor fault diagnosis, another system operating mode is explored, which only transmits the fault diagnosis result when a fault happens or at a fixed interval. For this mode, the node lifetime reaches 73 days if sensor nodes transmit diagnosis results once per hour.