Condition Monitoring on the Way to Industry 4.0
Condition Monitoring creates the substructure of what has become known as Industry 4.0. In its primary form, the term is relatively self-explanatory and simply monitoring the condition of an asset.
Within the Industrial IoT ecosystem, an essential part of Condition Monitoring is providing data that can be utilized for Predictive Maintenance and other smart factory applications such as Digital Twin which is a digital copy of a living or non-living physical existence. Condition-based monitoring allows early detection and identification of machine and system irregularities in real time.
Vibration, current and temperature all theses parameters gives key insights into the health of equipment varying from motors and pumps to bearings and encoders. These machine health insights result in improved efficiency, increased coherence and maximized uptime, accelerating the pavement to the Industry 4.0.
Before the emergence of Industry 4.0, usually scheduled preventive maintenance was the primary method used to reduce downtime and assure smooth equipment operation. For preventative maintenance, workers must be regularly trained to understand machine breakdowns. Discovering the actual maintenance schedule can be hard, sometimes leading to over-maintenance of machines that could, ironically, enhance the amount of unnecessary downtime.
As per ITIC survey, 98% of establishments report losing at least $100,000 per hour of downtime, out of those 33% surveyed claiming to lose from $1 million to $5 million per hour. Therefore, any maintenance implemented on machines must be geared toward downtime reduction.
These IoT predictive maintenance schemes fall under condition monitoring (CM), which is the process of remarking machinery condition parameters in order to recognize changes that are representative of a developing fault. Efficient CM addresses conditions that would reduce normal machine lifespan before they arrive into a major failure. As a result, CM is a major tool in minimizing downtime and assuring effective maintenance through IoT.
By monitoring the frequency, phase, position, amplitude and direction of vibrations in machinery, it is possible to recognize many faults. For example, an analyst can change wear on a specific gear or bearing, an absence of lubrication, variation, a misalignment, a loose mounting or an outage. Early diagnosis and preventative maintenance can also intercept more serious faults from developing.
Discrete manufacturing
The types of IoT-enabled condition monitoring deviate generally between machines and should be customized from application to application. As per report from Wall Street Journal, Industrial machinery equipment loss causes 42% of annual unplanned downtime and efficient IoT-enabled condition monitoring can do much to minimize machine breakdown.
IoT sensors either integrated into machine design or installed on legacy equipment can give operators with a huge range of performance metrics, involving pressure, vibration, temperature, voltage and battery charge.
CM technologies such as ultrasound material thickness testing, lubricant analysis, motor current signature analysis (MCSA) and infrared thermography can transfer data to a management platform that can monitor the data and identify damage that will lead to loss.
Automotive Fleets
Automotive downtime is a common challenge faced by companies that uses fleets. As per Automotive Fleet report, every hour of automobile downtime results in an average loss of $79.32 per hour, per driver, without factoring in the expenses sustained by making repairs. To minimize this downtime and more efficiently utilize maintenance resources, fleet managers have moved to CM IoT systems to analyze vehicle health.
Factors like engine temperature, vehicle vibration and fuel utilization can be measured to recognize potential faults before they happen. When any condition shifts beyond its standard operating threshold, the sensors can activate alerts and redirect the vehicle to a service center.
Condition Monitoring IoT systems also can construct a profile of each fleet driver to analyze their effect on the vehicle’s condition, which is helpful for controlling driver risk and determining any operator weakness.
Process manufacturing
In the steel industry, condition monitoring can be implemented to analyze the state of cold rolling mills, specifically crucial for the quality of steel and hard to examine with portable instruments. Early detection of rolling mills’ defects empowers manufacturers to timely take curative actions and reduce the negative effect on the product output.
In pulp and paper industry, condition monitoring is utilized to trace the condition and the arrangement of rolls and roll balance. For that, sensors receive the data on vibration and temperature and transfer it through the gateway to the cloud for analysis.
Upstream oil and gas
The capacity to analyze offshore drillings in real time from a single location is the reason behind upstream oil and gas companies move to Industrial IoT development. They turn out IoT solutions to analyze the condition of drilling equipment, storage tanks, pressure vessels, pipelines, etc.
For that, a network of sensors is located to receive the data about drilling equipments condition. Once the data is prepared, the condition monitoring solution is suitable to recognize the equipment in the potentially significant state. Additionally, drilling rigs expand high sensitivity IR cameras to control pipeline leakages by recognizing differences in temperatures.
Electric power
Condition monitoring enables power plants to assure reliable power generation. In the electric power industry, it is implemented to follow the health of coal-fed steam turbines, wind turbines, electrical substations, gas turbines and even nuclear power plants. With vibration and pulse shock sensors, it is likely to control the condition of, for example, a turbines’ rotating parts, cages and gearbox bearings.
Construction
IoT-based condition monitoring is broadly referred to follow the health and operating parameters of heavy machinery. With oil analysis, for example, it is desirable to control the state of a combustion engine and recognize early-stage problems. For example, the increased contents of iron warns about engine decay, while the combination of iron, aluminum may warn about upper cylinder wear.
Condition monitoring Techniques
Here are some of the most commonly used Condition Monitoring techniques:
• Vibration analysis
• Lubricant analysis
• Infrared thermography
• Acoustic emission
• Ultrasound
• Motor current signature analysis (MCSA)
• Model-based voltage and current systems (MBVI)
In above flow diagram, after running a voltage through a motor the current is considered and compared to that of a mathematical model that is fed with exact real-time data from the same motor. The two current readings are summarized and compared. In cases where no variations are clear-cut, the system can be considered as healthy.
If there are deviations between the mathematical model and the actual motor, we proceed with the analysis stage to search out absolutely what the problem is. Once the problem has been recognized, we can classify it and establish the relevant solution.
In this example, the concept of the continuity of Condition Monitoring becomes clear. It makes sense to continuously be able to analyze and record the motor’s status, instead of only temporarily executing a diagnostics check. This way, historical trends are reproduced automatically showing us how mechanical, electrical and operational problems and their parameters change over time.
There are two important methods utilized for condition monitoring
• Trend monitoring
This is the constant or regular measurement and clarification of data. It includes the selection of a suitable and quantifiable indication of machine or component failure and the study of the trend in this computation with running time to specify when deterioration exceeds a critical rate.
• Condition checking
In this method a check measurement is taken with the machine running, using some acceptable indicator and this is then used as a measure of the machine condition at that time.
Breakdown or erosion of material results in material loss, which finally results in leakage and accidents that prove costly and therefore, must be avoided. Regular condition monitoring removes this, as suitable action can be taken earlier to considerable damage occurring.
Condition Monitoring Software
With sensors it is easy to remark different parameters of the machines as they work, it would also be very helpful to have an application to focus the information and convey the required action. For this reason, the adoption of condition monitoring software is expanding rapidly as manufacturers consider for an easy and effective way to clarify information assembled by a CM system and then take suitable action upon it.
The remote condition monitoring is a great example of such software with the exception that it not only combines the Condition Monitoring data but also assists the planning and distribution of an entire condition monitoring system from scratch.
Where the sensors should be placed? What they should be measuring? How they should be calibrated? What alerts they should send out and to where? All these questions can be answered using Condition Monitoring software, enabling stakeholders to consider on the design of a system at any stage.
Once remote Condition Monitoring has been deployed, the software starts to act as its hub, collecting all the incoming data being described by the sensors into a central repository, enabling for deep data analysis that drives curative action.
Types of Condition Monitoring
Condition Monitoring of Machines is critical as it supports to discover information regarding the health of any machine. These early information can be utilized to recognize warnings which can assist technicians to prevent unscheduled electrical failure and also minimize repair and maintenance cost. It helps to optimize machine performance and eliminate risks of accidental failures. There are different types of machine condition monitoring. Below are few of them:
Route Monitoring
The technician measures and records the machine data regularly with a handheld device. Such data is then examined to control whether advanced analysis is required or not.
Portable Machine Analysis
PMA (Portable Machine Analysis) is a system where portable devices are utilized to analyze the health of machines. Typically, sensors are permanently connected to such machines and data acquisition devices are used to fetch and gather data.
Factory Assertion Test
FAT (Factory Assertion Test) is utilized to assure that finished goods encounter the predefined design norms and record feasible failure modes of the equipment.
Online Machine Monitoring
OMM (Online Machine Monitoring) is the mechanism of analyzing and recognizing machine equipments and tools as it is in running state. In such system, embedded devices and servers are utilized for data acquisition, maintenance scheduling and analysis.
Online Machine Protection
OMP (Online Machine Protection) is the routine of constantly analyzing machine equipment as it runs. Embedded devices are utilized to gain and monitor the vast amount of data that machines release. Machines can also be turned on/off by setting mechanism.
Advantages of IoT Condition Monitoring
Condition Monitoring supplies multiple business advantages, including secondary advantages earned from the reduction in costs and resources that this technology allows. The core benefits of condition monitoring can be summarized as follows:
Minimized maintenance costs
Maintenance becomes dynamic and well timed, cutting labor and travel costs and repairs are done before evaluative damage occurs. Service time is minimized and customer satisfaction improved.
Maximized production
With correct and large readouts from sensors on production machines, integrated with data analytics algorithms to acquire visibility into production inefficiencies, new levels of efficiency can be reached. This is particularly true with condition monitoring in the oil and gas industry.
Optimized inventory of spare parts
Instead of overstocking the inventory of costly spare parts, which affects margins or running low on inventory, which improves downtime, Condition Monitoring allows actual prediction of the demand for spare parts.
Precise and relevant data for driving product development
Asset behavioral data gathered over time can be combined and monitored by engineering to recognize product design faults that can be resolved in successive product versions.
Increased machinery lifetime
The health of a machine and all of its components is examined in detail. Overheating, wear-and-tear and other warnings to the machine’s well-being are taken care of in a timely manner that extend machine’s lifespan.
The precision and depth of the data gathered from Condition Monitoring and its reach to enclosing a complete factory or plant, supplies manufacturers with especially valuable information that can be leveraged to make well informed business decisions, to minimize the effect of the losses.
Condition Monitoring fits into the general Industry 4.0 framework as a basic block for constant improvement. IoT-based condition monitoring supplies a strong foundation for process and ROI optimization.