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Prashant Kapadia
Vibration Sensors Are Essential to Maintaining Machine Health
Aid root cause analysis by detecting wear and tear in advance
Published: Monday, August 8, 2022 – 12:03
Workforce scarcity and remote employment made it challenging to maintain industrial machinery during and after the Covid-19 epidemic. With the global industrial automation market expected to nearly double in the next six years, maintaining an increasing number of assets will result in more unscheduled downtime, higher production losses, and detrimental effects on long-term machine health. Many firms have switched to calendar-based maintenance to avoid this.
However, the rapid advancements in shop-floor technology and our ability to acquire, interpret, and apply data have allowed us to connect and monitor our equipment, gather data, and learn how to care for it. According to leading business and economics research establishment McKinsey Global Institute, manufacturing companies could reduce product development costs by as much as 50 percent, and operating costs by as much as 25 percent, if they fully utilized the data available. But many plants still work on the “run to failure” maintenance strategy. Hence, understanding the ways machines “speak” is essential.
One way devices communicate to us is through vibrations. Machine-health monitoring and predictive maintenance for industrial equipment are made possible by industrial vibration-sensor technology. Machine vibration sensors use an accelerometer to measure and communicate data about the vibration of spinning machinery. Vibration data are critical in caring for our machines, reducing industry downtime, saving costs, and increasing efficiency. Vibration sensors are commonly used in rotating equipment such as pumps, motors, fans, compressors, gearboxes, gas turbines, conveyor bearings, and wind turbine gears, and are essential in automotive production, water treatment, food processing, building automation, oil and gas, chemical plants, and the power industry.
What are vibration sensors?
Several methods are used to diagnose machine health using modern technologies. Vibration testing is one of the most effective. During the early 1900s, the first rudimentary vibration sensors were produced. Plant-monitoring applications now use advanced vibration sensors, which detect and measure vibration in a system, machine, or piece of rotating equipment.
Vibration analysis is defined as measuring the vibration levels and frequencies of machinery and then using that information to analyze how healthy the machines and their components are. Although the inner workings and formulas used to calculate various forms of vibration can get complicated, it all starts with using an accelerometer to measure pulse. Anytime a piece of machinery is running, it’s making vibrations.
A vibration sensor is attached directly to an item. The sensor will detect vibrations from that asset in various ways after it’s installed. Sensors track changes in the velocity of a particular component. Any vibrations that occur while the sensor is mounted to a piece of equipment will cause the sensor to emit an electrical signal. PLC, BMS, or 4-20 mA input modules collect data locally or remotely.
Although piezoelectric accelerometers were frequently used in industrial applications, microelectromechanical systems (MEMS) sensors have more recently emerged as an effective solution. Fabricated on a silicon substrate, these polysilicon structures are based on cells comprising a movable plate between two fixed scales. MEMS capture the physical vibration signals and convert them into digital form by using an analog-to-digital converter integrated into the MEMS. The digital signal then goes through several filtering processes that remove the noise. In the end, the signal is converted into an acceleration milli-g unit the user can interpret for analysis.
Vibration sensors are key to maintaining machine health
Vibration sensors capture three forms of data: frequency, amplitude, and phase, in conjunction with speed/phase reference sensors or other vibration sensors. The frequency element of the data reveals how often the vibration occurs (related to the location of the problem in the machine). The frequency is the number of cycles that a vibrating object completes in one second. Amplitude data are the level or magnitude of the vibration in a piece of equipment (which is related to the fault severity). All of these data are important for machine maintenance. Here are the reasons.
Understanding causes of damage
Every rotating object or element in a machine will create vibrations that have a normal frequency and amplitude for that equipment. But it’s possible to detect abnormal vibrations based on the magnitude of the object’s specific frequency. Any abnormality of a component in the machine will produce higher-magnitude energy in a particular frequency. If the component is deteriorating, abnormal vibrations occur more frequently. An example would be roller element bearings. If the amplitude of a specific frequency for a component increases and contributes considerable energy to the overall vibrations, this component should be examined to determine the cause (e.g. a worn bearing).
The phase indicates the location of the vibration peak about a single shaft or rotor revolution. Phase analysis reveals information about relative motion between components. This is an additional tool to diagnose the fault and make correct decisions related to the suspected fault location, which is helpful for fault diagnostics and rotor balance.
Every time a machine is used, it develops wear and tear. The machine’s efficiency degrades with time. A vibration sensor installed on the device can aid root cause analysis (RCA) by detecting wear and tear in advance. The vibration sensor aids in preventive action that can benefit the machine’s efficiency and health.
Predictive maintenance
The potential failure-to-functional failure interval (P-F interval) is the time between the first indicators of a prospective failure and the occurrence of the functional loss. Based on data gathered from vibration sensors, we may analyze changes in component states early and prevent breakdown using the P-F diagram.
While vibration monitoring can aid in root cause analysis, it’s most effective when employed in predictive maintenance. With vibration analysis, we may track vibration data in real time, spot dangerously high vibrations in the data, and recommend that the associated asset be repaired. Purchasing and storing spare parts in the industrial business can cost millions of dollars if done incorrectly. On the other hand, vibration sensors allow factory operators to monitor equipment health in real time. Factory employees can use vibration analysis to determine which sections of the machine are causing harm and when they should be removed or replaced.
Industries can forecast machine problems in advance and use components to their maximum potential by combining artificial intelligence and machine learning (AI/ML) with vibration sensors. Repairs can also be carried out before a breakdown, saving downtime and operational costs. The efficiency of any rotating machinery is boosted due to predictive maintenance using a sensor like VIBit, which has advanced AI/ML capabilities.
Benefits
With the right application of vibration sensors and software the following benefits can be realized.
Alerts and reports in real time: Maintenance personnel can optimize their operations with real-time information on equipment health. A vibration sensor combined with an excellent cloud-based machine-health management platform can assist in delivering real-time machine alarms and details on how to optimize machine efficiency.
Prescriptive maintenance: This type of maintenance takes predictive maintenance a step further. Using AI/ML, it can prescribe how to fully use components identified in predictive maintenance, perhaps by changing certain machine operating parameters. This helps ensure that repairs are made before a breakdown occurs. We employ vibration sensors to monitor an asset and its elements to achieve this. It will lower costs and increase uptime.
Proactive maintenance: Proactive maintenance requires thorough machine inspection and condition monitoring. We can detect and eliminate failure by using vibration data analysis to identify root causes in advance. Misalignment, imbalance, and operator mistakes are all addressed ahead of time. As a result, the life of the machinery involved will be extended.
Need and benefits of condition-based maintenance technology: In contrast to traditional corrective maintenance or scheduled maintenance, condition-based maintenance (CBM) is an approach whereby maintenance is performed proactively on evidence of need, which is identified through direct monitoring and predictive analytics of the asset. To that end, specific knowledge of the asset’s condition is obtained by analyzing sensor information at any time during the asset’s working life. Maintenance can then be planned with sufficient lead time to minimize the cost and operational effect of a failure.
About The Author
Prashant Kapadia
Prashant Kapadia has 24 years of experience in industrial automation, embedded product, industrial software application design, development, manufacturing, and global business development with companies such as GE Digital, Hitachi, and currently CIMCON Digital. He has been part of the digital transformation of many Fortune 500 organizations.
Bio
Published: Monday, August 8, 2022 – 12:03
Workforce scarcity and remote employment made it challenging to maintain industrial machinery during and after the Covid-19 epidemic. With the global industrial automation market expected to nearly double in the next six years, maintaining an increasing number of assets will result in more unscheduled downtime, higher production losses, and detrimental effects on long-term machine health. Many firms have switched to calendar-based maintenance to avoid this.
However, the rapid advancements in shop-floor technology and our ability to acquire, interpret, and apply data have allowed us to connect and monitor our equipment, gather data, and learn how to care for it. According to leading business and economics research establishment McKinsey Global Institute, manufacturing companies could reduce product development costs by as much as 50 percent, and operating costs by as much as 25 percent, if they fully utilized the data available. But many plants still work on the “run to failure” maintenance strategy. Hence, understanding the ways machines “speak” is essential.
One way devices communicate to us is through vibrations. Machine-health monitoring and predictive maintenance for industrial equipment are made possible by industrial vibration-sensor technology. Machine vibration sensors use an accelerometer to measure and communicate data about the vibration of spinning machinery. Vibration data are critical in caring for our machines, reducing industry downtime, saving costs, and increasing efficiency. Vibration sensors are commonly used in rotating equipment such as pumps, motors, fans, compressors, gearboxes, gas turbines, conveyor bearings, and wind turbine gears, and are essential in automotive production, water treatment, food processing, building automation, oil and gas, chemical plants, and the power industry.
Several methods are used to diagnose machine health using modern technologies. Vibration testing is one of the most effective. During the early 1900s, the first rudimentary vibration sensors were produced. Plant-monitoring applications now use advanced vibration sensors, which detect and measure vibration in a system, machine, or piece of rotating equipment.
Vibration analysis is defined as measuring the vibration levels and frequencies of machinery and then using that information to analyze how healthy the machines and their components are. Although the inner workings and formulas used to calculate various forms of vibration can get complicated, it all starts with using an accelerometer to measure pulse. Anytime a piece of machinery is running, it’s making vibrations.
A vibration sensor is attached directly to an item. The sensor will detect vibrations from that asset in various ways after it’s installed. Sensors track changes in the velocity of a particular component. Any vibrations that occur while the sensor is mounted to a piece of equipment will cause the sensor to emit an electrical signal. PLC, BMS, or 4-20 mA input modules collect data locally or remotely.
Although piezoelectric accelerometers were frequently used in industrial applications, microelectromechanical systems (MEMS) sensors have more recently emerged as an effective solution. Fabricated on a silicon substrate, these polysilicon structures are based on cells comprising a movable plate between two fixed scales. MEMS capture the physical vibration signals and convert them into digital form by using an analog-to-digital converter integrated into the MEMS. The digital signal then goes through several filtering processes that remove the noise. In the end, the signal is converted into an acceleration milli-g unit the user can interpret for analysis.
Vibration sensors capture three forms of data: frequency, amplitude, and phase, in conjunction with speed/phase reference sensors or other vibration sensors. The frequency element of the data reveals how often the vibration occurs (related to the location of the problem in the machine). The frequency is the number of cycles that a vibrating object completes in one second. Amplitude data are the level or magnitude of the vibration in a piece of equipment (which is related to the fault severity). All of these data are important for machine maintenance. Here are the reasons.
Understanding causes of damage
Every rotating object or element in a machine will create vibrations that have a normal frequency and amplitude for that equipment. But it’s possible to detect abnormal vibrations based on the magnitude of the object’s specific frequency. Any abnormality of a component in the machine will produce higher-magnitude energy in a particular frequency. If the component is deteriorating, abnormal vibrations occur more frequently. An example would be roller element bearings. If the amplitude of a specific frequency for a component increases and contributes considerable energy to the overall vibrations, this component should be examined to determine the cause (e.g. a worn bearing).
The phase indicates the location of the vibration peak about a single shaft or rotor revolution. Phase analysis reveals information about relative motion between components. This is an additional tool to diagnose the fault and make correct decisions related to the suspected fault location, which is helpful for fault diagnostics and rotor balance.
Every time a machine is used, it develops wear and tear. The machine’s efficiency degrades with time. A vibration sensor installed on the device can aid root cause analysis (RCA) by detecting wear and tear in advance. The vibration sensor aids in preventive action that can benefit the machine’s efficiency and health.
Predictive maintenance
The potential failure-to-functional failure interval (P-F interval) is the time between the first indicators of a prospective failure and the occurrence of the functional loss. Based on data gathered from vibration sensors, we may analyze changes in component states early and prevent breakdown using the P-F diagram.
While vibration monitoring can aid in root cause analysis, it’s most effective when employed in predictive maintenance. With vibration analysis, we may track vibration data in real time, spot dangerously high vibrations in the data, and recommend that the associated asset be repaired. Purchasing and storing spare parts in the industrial business can cost millions of dollars if done incorrectly. On the other hand, vibration sensors allow factory operators to monitor equipment health in real time. Factory employees can use vibration analysis to determine which sections of the machine are causing harm and when they should be removed or replaced.
Industries can forecast machine problems in advance and use components to their maximum potential by combining artificial intelligence and machine learning (AI/ML) with vibration sensors. Repairs can also be carried out before a breakdown, saving downtime and operational costs. The efficiency of any rotating machinery is boosted due to predictive maintenance using a sensor like VIBit, which has advanced AI/ML capabilities.
With the right application of vibration sensors and software the following benefits can be realized.
Alerts and reports in real time: Maintenance personnel can optimize their operations with real-time information on equipment health. A vibration sensor combined with an excellent cloud-based machine-health management platform can assist in delivering real-time machine alarms and details on how to optimize machine efficiency.
Prescriptive maintenance: This type of maintenance takes predictive maintenance a step further. Using AI/ML, it can prescribe how to fully use components identified in predictive maintenance, perhaps by changing certain machine operating parameters. This helps ensure that repairs are made before a breakdown occurs. We employ vibration sensors to monitor an asset and its elements to achieve this. It will lower costs and increase uptime.
Proactive maintenance: Proactive maintenance requires thorough machine inspection and condition monitoring. We can detect and eliminate failure by using vibration data analysis to identify root causes in advance. Misalignment, imbalance, and operator mistakes are all addressed ahead of time. As a result, the life of the machinery involved will be extended.
Need and benefits of condition-based maintenance technology: In contrast to traditional corrective maintenance or scheduled maintenance, condition-based maintenance (CBM) is an approach whereby maintenance is performed proactively on evidence of need, which is identified through direct monitoring and predictive analytics of the asset. To that end, specific knowledge of the asset’s condition is obtained by analyzing sensor information at any time during the asset’s working life. Maintenance can then be planned with sufficient lead time to minimize the cost and operational effect of a failure.
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Prashant Kapadia has 24 years of experience in industrial automation, embedded product, industrial software application design, development, manufacturing, and global business development with companies such as GE Digital, Hitachi, and currently CIMCON Digital. He has been part of the digital transformation of many Fortune 500 organizations.
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