New Electric Online Oil Condition Monitoring Sensor - an Innovation in Early Failure Detection of Industrial Gears (original) (raw)
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Lubricating oil conditioning sensors for online machine health monitoring – A review
Tribology International, 2017
Analysis of lubricating oil is an effective approach in judging machine's health condition and providing early warning of machine's failure progression. Many studies from both academia and industry have been conducted. This paper presents a comprehensive review of the state-of-the-art online sensors for measuring lubricant properties (e.g. wear debris, water, viscosity, aeration, soot, corrosion, and sulfur content). These online sensors include single oil property sensors based on capacitive, inductive, acoustic, and optical sensing and integrated sensors for measuring multiple oil properties. Advantages and disadvantages of each sensing method, as well as the challenges for future developments, are discussed. Research priorities are defined to address the industry needs of machine health monitoring.
Control Systems and Optimization Letters, 2023
Every year high costs are expending to repair rotating machinery in factories and industrial centers due to failures. Most failures happen suddenly while by condition monitoring of systems prognosis and diagnosis are possible. By condition monitoring, the failures can be detected and solved in the early stages. Gearboxes are an element used widely, and applying condition monitoring for them makes a significant benefit for saving budget due to prognosis and removing failure before progress. The current paper aims to present a condition monitoring system for gearbox which is able to inspect lubricant by oil temperature and pH. Moreover, it can detect some defects in the gearbox by vibration analyses such as unbalance, bent shaft, looseness in bearing housing, and whirl of oil. The evaluation of the system shows that its accuracy is proper for use in gearboxes.
On-line Oil Monitoring and Diagnosis
Strojniški vestnik – Journal of Mechanical Engineering, 2013
In condition monitoring (CM) of mechanical drives, the analysis of various physical and chemical properties of the operating lubricant can be used to diagnose defects and assess the state of the system. Recent developments in on-line oil condition sensors and advances in signal processing methods have allowed for a system for on-line oil analysis to be developed and applied in the field of predictive maintenance. The System for On-line Oil Analysis (SOOA) has the ability to measure multiple oil properties of interest and detect faults induced by transients in the acquired signals. Transient detection is based on the cumulative sum of errors (CUSUM) technique, where the error represents the difference between the predicted reference value and the current measured value. Detection of abnormal behaviour, based on transient detection, is followed by fault diagnosis, through integrated assessment of oil properties in real time. The system can operate as a standalone unit with an independent user interface or as a part of a complete integrated diagnostic system, merging oil condition evaluation with vibrational analysis and other techniques. This paper focuses on the algorithms within SOOA in charge of transient detection and fault diagnosis. The results of SOOA operation are presented through a demonstration of the method in a laboratory environment with two different sets of tests: gear pitting and water contamination.
Survey of lubrication oil condition monitoring, diagnostics, prognostics techniques and systems
Recently, an increasing demand for performance assessment of lubrication oil has been noticed. Considerable techniques and systems in lubrication oil condition monitoring have been developed and successfully utilized in many applications such as gasoline/diesel engines, gearboxes, etc. This paper provides a comprehensive review of the existing lubrication oil condition monitoring solutions and their characteristics along with the classification and evaluation of each technique. The reviewed techniques are analyzed and classified into four categories: electrical (magnetic), physical, chemical, and optical techniques. The characteristic of each solution and its sensing technique is evaluated with a set of properties which are crucial for oil health monitoring, diagnostics and prognostics. A comprehensive comparison among a wide range of different lubrication oil condition monitoring solutions is conducted.
Survey of lubrication oil condition monitoring, diagnostics, and prognostics techniques and systems
Recently, an increasing demand for performance assessment of lubrication oil has been noticed. Considerable techniques and systems in lubrication oil condition monitoring have been developed and successfully utilized in many applications such as gasoline/diesel engines, gearboxes, etc. This paper provides a comprehensive review of the existing lubrication oil condition monitoring solutions and their characteristics along with the classification and evaluation of each technique. The reviewed techniques are analyzed and classified into four categories: electrical (magnetic), physical, chemical, and optical techniques. The characteristic of each solution and its sensing technique is evaluated with a set of properties which are crucial for oil health monitoring, diagnostics and prognostics. A comprehensive comparison among a wide range of different lubrication oil condition monitoring solutions is conducted.
Demonstration of Sensor Monitoring of Lubricants
PHM Society Asia-Pacific Conference
Due to the spread of carbon neutral, the effective use of lubricant has been drawing attention. Since the main component of lubricant is petroleum-derived hydrocarbon oil, reducing the amount used by 1 kg will reduce CO2 by approximately 3 kg. The value of CO2 reduction is very important. In order to reduce the amount of lubricant used, there is a movement to reduce the frequency of lubricant exchange or continue to use lubricant without exchanging it. However, it is known that lubricant-induced mechanical failures occur. For this reason, equipment condition monitoring using oil sensors has been spread. The color of the lubricant, also called machine blood, indicates the condition of the machine. The oil sensor measures contamination, which has a fatal effect on machine failure, and oxidation degradation, which is related to the performance of lubricant and the machine failure. Contamination includes water and wear debris, and oxidative degradation includes consumption of additives ...
Lubrication oil condition monitoring and remaining useful life prediction with particle filtering
In order to reduce the costs of wind energy, it is necessary to improve the wind turbine availability and reduce the operational and maintenance costs. The reliability and availability of a functioning wind turbine depend largely on the protective properties of the lubrication oil for its drive train subassemblies such as gearbox and means for lubrication oil condition monitoring and degradation detection. The wind industry currently uses lubrication oil analysis for detecting gearbox and bearing wear but cannot detect the functional failures of the lubrication oils. The main purpose of lubrication oil condition monitoring and degradation detection is to determine whether the oils have deteriorated to such a degree that they no longer fulfill their functions. This paper describes a research on developing online lubrication oil health condition monitoring and remaining useful life prediction with particle filtering technique using commercially available online sensors. The paper firs...
Mechanical Systems and Signal Processing, 2011
The monitoring of progressive wear in gears using various non-destructive technologies as well as the use of advanced signal processing techniques upon the acquired recordings to the direction of more effective diagnostic schemes, is the scope of the present work. For this reason multi-hour tests were performed in healthy gears in a single-stage lab scale gearbox until they were seriously damaged. Three on-line monitoring techniques are implemented in the tests. Vibration and acoustic emission recordings in combination with data coming from oil debris monitoring (ODM) of the lubricating oil are utilized in order to assess the condition of the gears. A plethora of parameters/features were extracted from the acquired waveforms via conventional (in time and frequency domain) and nonconventional (wavelet-based) signal processing techniques. Data fusion was accomplished in the level of integration of the most representative among the extracted features from all three measurement technologies in a single data matrix. Principal component analysis (PCA) was utilized to reduce the dimensionality of the data matrix whereas independent component analysis (ICA) was further applied to identify the independent components among the data and correlate them to different damage modes of the gearbox. Finally heuristic rules based on characteristic values of the resulted independent components were set, realizing thus a health monitoring scheme for gearboxes.
International advanced research journal in science, engineering and technology, 2021
Gear is the key part of the gearbox and it is important to monitor the working condition of the gear in the fault diagnosis of the gearbox. The progress and changes over the past 30 years in failure detection techniques of rotating machinery changes frequently. Oil analysis technique and vibration monitoring both are widely used in the field of fault diagnosis and condition monitoring. The purpose of this paper is to compare two systems for monitoring the condition of a gearbox.