Search results for "condition monitoring"
showing 10 items of 41 documents
Novel Threshold Calculations for Remaining Useful Lifetime Estimation of Rolling Element Bearings
2018
The prognostics objective is to avoid sudden machinery breakdowns and to estimate the remaining useful life after initial degradation. Typically, physical health indicators are derived from available sensor data, and a mathematical model is tuned to fit them. The time it takes for the model to reach a failure threshold is the estimated remaining useful life. The failure threshold may be determined from historical failure data, but that is not always readily available. ISO standard 10816–3 defines permissible velocity vibration levels for machines that may be used as a failure threshold. However, velocity vibration is not suitable for bearing prognostics due to the effect of integration from…
Cross-correlation of whitened vibration signals for low-speed bearing diagnostics
2019
Abstract Rolling-element bearings are crucial components in all rotating machinery, and their failure will initially degrade the machine performance, and later cause complete shutdown. The period between an initial crack and complete failure is short due to crack propagation. Therefore, early fault detection is important to avoid unexpected machine shutdown and to aid in maintenance scheduling. Bearing condition monitoring has been applied for several decades to detect incipient faults at an early stage. However, low-speed conditions pose a challenge for bearing fault diagnosis due to low fault impact energy. To reliably detect bearing faults at an early stage, a new method termed Whitened …
Accelerated bearing life-Time test rig development for low speed data acquisition
2017
Condition monitoring plays an important role in rotating machinery to ensure reliability of the equipment, and to detect fault conditions at an early stage. Although health monitoring methodologies have been thoroughly developed for rotating machinery, low-speed conditions often pose a challenge due to the low signal-to-noise ratio. To this aim, sophisticated algorithms that reduce noise and highlight the bearing faults are necessary to accurately diagnose machines undergoing this condition. In the development phase, sensor data from a healthy and damaged bearing rotating at low-speed is required to verify the performance of such algorithms. A test rig for performing accelerated life-time t…
Health Indicator for Low-Speed Axial Bearings Using Variational Autoencoders
2020
This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using Variational Autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the sugg…
Multi-band identification for enhancing bearing fault detection in variable speed conditions
2020
Abstract Rolling element bearings are crucial components in rotating machinery, and avoiding unexpected breakdowns using fault detection methods is an increased demand in industry today. Variable speed conditions render a challenge for vibration-based fault diagnosis due to the non-stationary impact frequency. Computed order tracking transforms the vibration signal from time domain to the shaft-angle domain, allowing order analysis with the envelope spectrum. To enhance fault detection, the bearing resonance frequency region is isolated in the raw signal prior to order tracking. Identification of this region is not trivial but may be estimated using kurtosis-based methods reported in the li…
Visualizing Time Series State Changes with Prototype Based Clustering
2009
Modern process and condition monitoring systems produce a huge amount of data which is hard to analyze manually. Previous analyzing techniques disregard time information and concentrate only for the indentification of normal and abnormal operational states. We present a new method for visualizing operational states and overall order of the transitions between them. This method is implemented to a visualization tool which helps the user to see the overall development of operational states allowing to find causes for abnormal behaviour. In the end visualization tool is tested in practice with real time series data collected from gear unit.
Continuous Monitoring of Parasitic Elements in Boost Converter Circuit
2021
The given paper explains the necessity of condition monitoring for DC/DC boost converter circuit. Further, an analytical model of circuit parasitic estimation is presented based on measured quantities in the circuit. The implementation of continuous estimation of circuit parasitic elements is analytically explained and verified by simulations and experimental results. Obtained results are acceptable for condition monitoring.
Challenges of the "Global Understanding Environment" Based on Agent Mobility
2007
Among traditional users of Web resources, industry also has a growing set of smart industrial devices with embedded intelligence. Just as humans do, smart industrial devices need online services—for example, for condition monitoring, remote diagnostics, maintenance, and so on. In this chapter, we present one possible implementation framework for such Web services. Assume that such services should be Semantic-Web-enabled and form a service network based on internal and external agents’ platforms, which can host heterogeneous mobile agents and coordinate them to perform needed tasks. The concept of a “mobile-service component” assumes not only the exchange of queries and service responses but…
Optical method based detection and wavelets based processing of acoustic waves
2020
Acoustic waves (AW) has been used for the testing of static and dynamic structures. They contain the signature about the performance of rotary machines such as cyclic fatigue, friction, turbulence and cavitation. Thus has been extensively used in the condition monitoring and material characterization. In this paper, we present an algorithm based on wavelets to process the transient AW in time and frequency domain both simultaneously to extract its the temporal (e.g. time duration) and spectral properties (e.g. emission frequency). Further, optical method based on optical feedback (OF) is presented for detection of AW providing powerful non-contact, non-destructive diagnostic capabilities, w…
Towards Low-Cost Pavement Condition Health Monitoring and Analysis Using Deep Learning
2020
Governments are faced with countless challenges to maintain conditions of road networks. This is due to financial and physical resource deficiencies of road authorities. Therefore, low-cost automated systems are sought after to alleviate these issues and deliver adequate road conditions for citizens. There have been several attempts at creating such systems and integrating them within Pavement management systems. This paper utilizes replicable deep learning techniques to carry out hotspot analyses on urban road networks highlighting important pavement distress types and associated severities. Following this, analyses were performed illustrating how the hotspot analysis can be carried out to…