One of the main tools of operational reliability in the industry is the use of proactive and predictive maintenance, whose objectives are to detect and predict events in machinery and systems that may interfere with the production process and take actions to avoid them, for which they use different techniques and tools to develop their work. As plants and their assets are being digitized and moving towards Industry 4.0, new scenarios are emerging for technological advances in tribology and condition monitoring.
Lubricants and hydraulics are critical fluids for any type of machinery. Many diverse studies have demonstrated that fluid cleanliness is one of the most powerful strategies to eliminate root cause failures of critical assets such as power generation units, hydraulic systems, air compressors and gearboxes. On-line monitoring for critical fluids can be strategically deployed to provide early failure diagnosis, supporting accurate engineering decisions to improve reliability and cost savings.
On-line Oil Cleanliness Technology
Nowadays and worldwide, lubricant condition monitoring philosophy is beginning to be integrated into Industry 4.0 using different types of monitoring techniques such as bottle sample routine with laboratory analysis, on-site laboratory routines with automatic instruments, visual field inspections and real-time monitoring through on-line sensors. Unless companies have their own laboratories, a subcontracted laboratory must be hired, and the samples moved to their location. All types of samples, either of used or new oil, should be taken carefully in challenging environments, using the proper techniques and devices to get accurate and representative results. Laboratory analysis can take a couple of days to get results after the sample arrives, which means it takes considerably more time to know the fluid condition and take timely actions.
Emerging sensor technologies have evolved over traditional lubricant condition monitoring techniques in such a way that integrating laboratory analysis with data generated on-line will help lubrication engineers extend the life of machines. A sensor can be defined as a device that transforms a mechanical, chemical, movement, pressure, temperature or other signal into an electrical signal to be detected by a control system. There are different types of sensors for each application; the important thing is their detection process by which they transform the signals. Among the most common, we find inductive, capacitive, optical and ultrasonic sensors.
Table 1. On-line Oil Cleanliness Technologies. RAMGUZ
Real-time sensing in critical machinery fluids brings the benefit of timely detection of a problem associated with lubrication, contamination or operational conditions. The timely detection allows for prompt actions, such as planned inspection, validation through other predictive techniques or starting a filtration or water removal process. What follows is a summary of current lubricant cleanliness monitoring technologies.
During the past two decades, there have been different developments and improvements in the principles of oil cleanliness devices and sensors. Portability, accuracy, interface experience and on-line capacities provide the lubricant teams with an easier-to-use and flexible tool. Automatic particle counters based on light extinction are still the most common method used by the industry for particle contamination analysis. (Fig 1) As a particle passes through a light beam, the light intensity received by a photo-detector is reduced in proportion to the size of the particle. Special care should be taken to mitigate inaccuracies due to air and water bubbles, which are very common in lubrication circuits of the machinery.
Figure 1. Light Extinction Principle. Pall Corporation
Figure 2. Mesh Blockage Principle. Pall Corporation
Mesh blockage devices are an effective alternative to light extinction, especially in conditions where the fluid is opaque or where free water or air bubbles are present. (Fig 2) Mesh blockage devices determine particulate contamination levels by passing a specified flow of sample fluid through a series of calibrated mesh screens in a specified sequence. Pressure drop build-up (or flow degradation) is dependent on particulate contamination levels. The mesh is cleaned by backflushing.
New technologies are emerging from the development of digital high-resolution image processing. (Fig 3) In this type of cutting-edge sensor, the particle passes between a light and a lens, and an integrated Complimentary Metal Oxide Semiconductor (CMOS) Sensor and processor acquires and automatically processes microscopic images of fluid contamination, detecting, quantifying and classifying the particles by size. Then, computing logarithms and artificial intelligence transform the images obtained into technical data.
It is possible to get the on-line cleanliness ISO4406 code with portable monitors or sensors connected to the machines. Depending on the type of application and the characteristics of the fluid, a particle monitor can be connected to a representative point of the system and display the data through a local screen, printer, USB memory or by extracting the data through a cable. In many cases, for critical machines, a sensor connected directly to a live turbulent pipe allows monitoring of the condition of contamination and wear downstream of the mechanical components, thus obtaining real-time readings on a computer or smart device.
The advances in air and water bubble detection should not be overlooked to avoid false readings, especially when there are low pressures, like in cases when sampling is needed in return point lines and external filter carts. Particularly interesting are the innovations to permanently quantify the lubricant color in such a way that changes can be contrasted and related with new oil color, and possible fluid degradation, varnish or cross-contamination can be detected. Furthermore, the possibility of measuring in real-time the size and quantity of particles, as well as the shape of each one, allows detecting trends to determine the root causes of wear present in the machine fluid.
The level of contamination with particles measured by on-line devices must allow the personnel to take fast actions regarding the lubrication program and contamination control strategies. It could be useful to coordinate the starting of kidney loop filters when the ISO4406 code is going above proactive limits until the cleanliness objective is achieved. The on-line monitoring of the differential pressure of the lubricant filters allows for the recording of the capture efficiency and contrast conditions of particle overloads to identify abnormal wear or contaminant ingress. Changing the filters by condition could also be done using these techniques.
In addition to particulate contamination, water contamination in lubricants can cause serious problems, reducing the health of the fluid and increasing the wear of the machine surfaces. Capacitive water sensors incorporate a probe that can be directly immersed in the fluid to monitor dissolved water content and temperature. The electrical resistance of the dielectric polymer changes as the relative humidity changes. Today it is also possible to measure the free and emulsified water content using Near-infrared (NIR) spectroscopy sensors. All states of water and different sizes and shapes of particle contamination in critical fluids can be monitored by attaching sensors to a strategic lubricant sampling point and integrating them into a complete architecture of an on-line monitoring solution.
Architecture of an On-line Fluid Cleanliness Installation
Real-time data from the sensors and monitors can be shared locally in the plant or company network through a PLC or SCADA control system or through an Industrial Internet of Things (IIoT) platform. The data collected should be properly stored and protected so as to know the current status of the fluid and to build new insights about fluid cleanliness and machinery health correlated with different operational information.
A typical installation includes at least four layers of connectivity and integration; firstly, a physical layer with different on-line IIoT devices and sensors measuring fluid contamination variables (particles, wear shape, water content, filter condition and oil degradation). At this point, it is also important to link operational variables such as temperature, RPM and pressure with fluid health variables such as viscosity and TAN. Predictive maintenance monitoring data like vibration can also be useful. One or more of these sensors should be properly selected for the operational and fluid conditions, building a pathway to avoid machinery failure root causes. The installation of the devices at representative sampling points is crucial to get the best possible value for the investment, ensuring autonomy, interoperability, security and simplicity.
A second layer, the network and communication layer, integrates all the logic controllers, gateways and supporting electronic and network devices. All these signals must be properly managed to form a comprehensive vision for the engineer or analyst in front of the data, ensuring coverage and security.
Figure 3. High-resolution image. Processing Principle.
The third layer is formed by the data storage and streaming processes that could be transferred into the cloud or local servers. At this point, different IT activities are deployed to complete a condition monitoring system, such as back and front-end design, live data dashboards and valuable condition alerts and trends that help the end-users in the plant notice the condition of their critical assets’ vital fluids.
There is a fourth, primal layer for industry 4.0 essence: managing data to get maximum value for sustainability. New efforts are directed towards the research and development of new predictive models in the lubrication of machinery and its relationship with the real-time cleanliness of fluids. The proposed architecture of an on-line cleanliness monitoring solution is effective when it is redirected into proactive and predictive maintenance actions to ensure a cost-effective proposal.
On-line Fluid Cleanliness Cases Studies
Case Study 1: Fluid Cleanliness Optimization: An open-pit mining maintenance department installed a new filtration system in the hydraulic hoist and brake circuit of a 240-ton haul truck to validate the effectiveness of a high-efficiency in-line filter. An on-line particle sensor was connected upstream of the new auxiliary filter to measure the progress of the ISO4406 cleanliness level in real-time.
On-line particle monitoring showed the complete process while the filtration update was taking place, from the start of the truck, followed by the initial flushing and calibration on the sensor, then the permanent filtration process until the truck stopped. End-user maintenance costs were also reduced, not only by the cost of hydraulic components but also by the use of in-line, onboard filtration, which avoids the need for servicing kidney loop filtration in the shops.
Case Study 2: Early Failure Detection: A wind power generator utilized an on-line particle sensor to monitor the cleanliness level of the gearbox lubricant. This sensor was useful to help monitor both the oil and filter conditions since the mechanical components were not easily accessible.
The real-time ISO code started to rise continuously, so the customer decided to stop for a detailed inspection (correlated to hours of operation). Thanks to the early failure detection of a gearbox, the customer did a minor repair with a cost of $17,500 instead of a fatal failure (which would’ve cost $522,000).
Opportunities and Recommendations
The real-time reporting sensors have the utility of detecting contaminants in fluids quickly and creating an early alert to the maintenance department about performance, warnings or abnormal conditions, which can be of great value for high-cost equipment or performance evaluation processes.
The on-line monitoring of the hydraulic and lubricating system cleanliness in real-time allows for a practical observation of the ISO4406 levels of the oil. This ensures that the sample results are representative of the oil's condition, increases the speed at which crews can respond to emerging problems and reduces the possibility that samples will be drawn from off-line, non-representative locations due to machine accessibility.
The utility of the cleanliness sensors and devices for data correlation can be achieved by initially identifying the failure modes and then evaluating how, with the sensor location, the data can collaborate to solve the problem. For example, if it is required to observe the condition of a critical component such as a piston pump, it is recommended to install the sensor downstream of the component so that the response is short and effective.
A robust architecture, including electronic and IIoT communications equipment, is required to guarantee its performance and service life. It is recommended to use an IoT system that at least has capabilities such as a backup battery, IP65 protection, data logger, digital outputs and CAN bus outputs and that meets SAE J1455 vibration guidelines (Sec 4.9.4.2 fig 6-8), MIL-STD-810G and shock MIL-STD-810G (Sec 516.6).
There are many opportunities regarding the analytical management of the data captured for all kinds of on-line sensors in critical machinery fluids, which opens up an interesting study space for the future.
