Condition Based Maintainance
The paradigm shifts from preventive or damage-dependent maintenance of machinery and equipment to condition-based maintenance (predictive) poses a difficult challenge for the industry. To succeed in cost-driven competition, this step is essential, as predictive maintenance can make more efficient use of machines and prevents unforeseen downtimes.
The most effective form of condition-based maintenance utilizes online or inline condition monitoring (CM). Due to the permanent recording of the machine condition, based on the measurement of physical quantities, increasing economic, ecological and safety pressure can be met. The necessary technical infrastructure in the form of powerful networks, which enable the seamless integration of additional sensors, are emerging or have already been established in the wake of Industry 4.0 and Industrial Internet of Things. Furthermore, the possibilities for process modeling and data evaluation due to the high level of algorithms (e.g., deep learning) and the meanwhile cheaply available computing power allow virtually unlimited potential for accurate machine monitoring.
The last step required to make predictive maintenance a breakthrough is to provide the appropriate sensor technology. The basic prerequisites for these systems however are very strict, due to the demands of highest measurement value stability with low or no maintenance of the sensors system itself required.
Oil Condition Monitoring
In the field of oil condition monitoring (OCM), this means that the sensor has to determine the smallest changes as well as the absolute values of the fluid parameters accurately, but is rarely affected by cross-sensitivities, aging effects and contamination. For the detection of the oil condition, the viscosity is the most important physical parameter, since it reflects the main lubricating property. The strong temperature dependence of the viscosity requires precise information on the temperature of the fluid that is to be observed.
The OCM solutions from MicroResonant use a low frequency resonant acoustic sensor element which can simultaneously measure density and viscosity with high accuracy. The vibrating sensor generates a flow profile that penetrates deeply into the liquid, whereby effects of surface adhesion of fluid components are less impairing than with higher frequency sensors such as surface acoustic wave resonators (SAW).
Precision – long-term stability – reliability, does it really matter ?
Sensors for online oil condition measurement (oOCM) have to operate for a long time and must be able to react early when measurement values begin to deviate from the conventional readings. By using a highly sensitive and long-term stable system, the warning threshold can be set accordingly lower which give the operator more time react.
The costs of maintenance are much lower at an early stage due to better planning and lower downtimes. The benefit of an oOCM system therefore increases overproportionately with the precision of the system.
Why temperature is important
In oil condition monitoring, a specific reference temperature is usually defined for monitoring temperature-dependent variables such as the viscosity at 40°C, for example. If this temperature cannot be exactly held constant at the sensor but can at least be measured, the readings can in principle be converted numerically to the desired reference temperature. However, such an extrapolation is subject to larger inaccuracies due to the usually not exactly known temperature dependence and the dynamic mismatch arising from different time constants of viscosity and temperature sensors.
MicroResonant has therefore developed the VDC measuring cell, a temperature-controlled measuring system in which the fluid temperature can be specified in a wide range and with highest accuracy.
This eliminates the need for conversion to reference temperature, and furthermore allows to determine temperature responses of parameters. These data in turn provide additional information on the oil condition.
Why accurate temperature sensing is important for long-term stability
The viscosity of oils is a strongly temperature-dependent property (typ. 4%/°C). If the temperature sensor readings are subject to a slow temporal drift, the associated apparent change in viscosity could be misinterpreted as ageing of the oil.
Temperature drifts of more than 0.25°C/year are not uncommon. This translates to an apparent drift of viscosity of about 1%/year. Due to harsh environmental conditions, these values are often even significantly exceeded.
In order to address this issue MicroResonant uses high quality temperature sensor elements specifically optimized for long-term stability as well as carefully designed precision electronic circuits.