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Predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to estimate when maintenance should be performed. This approach claims more cost savings over routine or time-based preventive maintenance, because tasks are performed only when warranted. Thus, it is regarded as condition-based maintenance carried out as suggested by estimations of the degradation state of an item. [1] [2]
The main appeal of predictive maintenance is to allow convenient scheduling of corrective maintenance, and to prevent unexpected equipment failures. By taking into account measurments of the state of the equipment, maintenance work can be better planned (spare parts, people, etc.) and what would have been "unplanned stops" are transformed to shorter and fewer "planned stops", thus increasing plant availability. Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with negative impact on environment, and optimized spare parts handling.
Predictive maintenance differs from preventive maintenance because it does take into account the current condition of equipment (with measurements), instead of average or expected life statistics, to predict when maintenance will be required. Machine Learning approaches are adopted for the forecasting of its future states. [3]
Some of the main components that are necessary for implementing predictive maintenance are data collection and preprocessing, early fault detection, fault detection, time to failure prediction, and maintenance scheduling and resource optimization. [4] Predictive maintenance has been considered to be one of the driving forces for improving productivity and one of the ways to achieve "just-in-time" in manufacturing. [5]
Predictive maintenance evaluates the condition of equipment by performing periodic (offline) or continuous (online) equipment condition monitoring. The ultimate goal of the approach is to perform maintenance at a scheduled point in time when the maintenance activity is most cost-effective and before the equipment loses performance within a threshold. This results in a reduction in unplanned downtime costs because of failure, where costs can be in the hundreds of thousands per day depending on industry. [6] In energy production, in addition to loss of revenue and component costs, fines can be levied for non-delivery, increasing costs even further. This is in contrast to time- and/or operation count-based maintenance, where a piece of equipment gets maintained whether it needs it or not. Time-based maintenance is labor intensive, ineffective in identifying problems that develop between scheduled inspections, and therefore is not cost-effective.
The "predictive" component of predictive maintenance stems from the goal of predicting the future trend of the equipment's condition. This approach uses principles of statistical process control to determine at what point in the future maintenance activities will be appropriate.
Most predictive inspections are performed while equipment is in service, thereby minimizing disruption of normal system operations. Adoption of predictive maintenance can result in substantial cost savings and higher system reliability. In today's dynamic landscape of service maintenance, prolonged repair processes present a significant challenge for organizations striving to maintain operational excellence. Extended downtime, increased Mean Time to Repair (MTTR), and production losses not only affect profitability but also disrupt service continuity and diminish customer satisfaction. As equipment ages and maintenance requirements intensify, the quest for innovative solutions becomes increasingly urgent.
Reliability-centered maintenance emphasizes the use of predictive maintenance techniques in addition to traditional preventive measures. When properly implemented, it provides companies with a tool for achieving lowest asset net present costs for a given level of performance and risk. [7]
One goal is to transfer the predictive maintenance data to a computerized maintenance management system so that the equipment condition data is sent to the right equipment object to trigger maintenance planning, work order execution, and reporting. [8] Unless this is achieved, the predictive maintenance solution is of limited value, at least if the solution is implemented on a medium to large size plant with tens of thousands pieces of equipment. In 2010, the mining company Boliden, implemented a combined Distributed Control System and predictive maintenance solution integrated with the plant computerized maintenance management system on an object to object level, transferring equipment data using protocols like Highway Addressable Remote Transducer Protocol, IEC61850 and OLE for process control.
To evaluate equipment condition, predictive maintenance utilizes nondestructive testing technologies such as infrared, acoustic (partial discharge and airborne ultrasonic), corona detection, vibration analysis, sound level measurements, oil analysis, and other specific online tests. A new approach in this area is to utilize measurements on the actual equipment in combination with measurement of process performance, measured by other devices, to trigger equipment maintenance. This is primarily available in collaborative process automation systems (CPAS). Site measurements are often supported by wireless sensor networks to reduce the wiring cost.
Vibration analysis is most productive on high-speed rotating equipment and can be the most expensive component of a PdM program to get up and running. Vibration analysis, when properly done, allows the user to evaluate the condition of equipment and avoid failures. The latest generation of vibration analyzers comprises more capabilities and automated functions than its predecessors. Many units display the full vibration spectrum of three axes simultaneously, providing a snapshot of what is going on with a particular machine. But despite such capabilities, not even the most sophisticated equipment successfully predicts developing problems unless the operator understands and applies the basics of vibration analysis. [9]
In certain situations, strong background noise interferences from several competing sources may mask the signal of interest and hinder the industrial applicability of vibration sensors. Consequently, motor current signature analysis (MCSA) is a non-intrusive alternative to vibration measurement which has the potential to monitor faults from both electrical and mechanical systems.
Remote visual inspection is the first non-destructive testing. It provides a cost-efficient primary assessment. Essential information and defaults can be deduced from the external appearance of the piece, such as folds, breaks, cracks, and corrosion. The remote visual inspection has to be carried out in good conditions with a sufficient lighting (350 LUX at least). When the part of the piece to be controlled is not directly accessible, an instrument made of mirrors and lenses called endoscope is used. Hidden defects with external irregularities may indicate a more serious defect inside.[ citation needed ]
Acoustical analysis can be done on a sonic or ultrasonic level. New ultrasonic techniques for condition monitoring make it possible to "hear" friction and stress in rotating machinery, which can predict deterioration earlier than conventional techniques. [10] Ultrasonic technology is sensitive to high-frequency sounds that are inaudible to the human ear and distinguishes them from lower-frequency sounds and mechanical vibration. Machine friction and stress waves produce distinctive sounds in the upper ultrasonic range. Changes in these friction and stress waves can suggest deteriorating conditions much earlier than technologies such as vibration or oil analysis. With proper ultrasonic measurement and analysis, it's possible to differentiate normal wear from abnormal wear, physical damage, imbalance conditions, and lubrication problems based on a direct relationship between asset and operating conditions.
Sonic monitoring equipment is less expensive, but it also has fewer uses than ultrasonic technologies. Sonic technology is useful only on mechanical equipment, while ultrasonic equipment can detect electrical problems and is more flexible and reliable in detecting mechanical problems.
Infrared monitoring and analysis has the widest range of application (from high- to low-speed equipment), and it can be effective for spotting both mechanical and electrical failures; some consider it to currently be the most cost-effective technology. Oil analysis is a long-term program that, where relevant, can eventually be more predictive than any of the other technologies. It can take years for a plant's oil program to reach this level of sophistication and effectiveness. Analytical techniques performed on oil samples can be classified in two categories: used oil analysis and wear particle analysis. Used oil analysis determines the condition of the lubricant itself, determines the quality of the lubricant, and checks its suitability for continued use. Wear particle analysis determines the mechanical condition of machine components that are lubricated. Through wear particle analysis, you can identify the composition of the solid material present and evaluate particle type, size, concentration, distribution, and morphology. [11]
The use of Model Based Condition Monitoring for predictive maintenance programs is becoming increasingly popular over time. This method involves spectral analysis on the motor's current and voltage signals and then compares the measured parameters to a known and learned model of the motor to diagnose various electrical and mechanical anomalies. This process of "model based" condition monitoring was originally designed and used on NASA's space shuttle to monitor and detect developing faults in the space shuttle's main engine. [12] It allows for the automation of data collection and analysis tasks, providing round the clock condition monitoring and warnings about faults as they develop. Other predictive maintenance methods are related to smart testing strategies. [13]
Telemetry is the in situ collection of measurements or other data at remote points and their automatic transmission to receiving equipment (telecommunication) for monitoring. The word is derived from the Greek roots tele, 'remote', and metron, 'measure'. Systems that need external instructions and data to operate require the counterpart of telemetry: telecommand.
The technical meaning of maintenance involves functional checks, servicing, repairing or replacing of necessary devices, equipment, machinery, building infrastructure, and supporting utilities in industrial, business, and residential installations. Over time, this has come to include multiple wordings that describe various cost-effective practices to keep equipment operational; these activities occur either before or after a failure.
Nondestructive testing (NDT) is any of a wide group of analysis techniques used in science and technology industry to evaluate the properties of a material, component or system without causing damage. The terms nondestructive examination (NDE), nondestructive inspection (NDI), and nondestructive evaluation (NDE) are also commonly used to describe this technology. Because NDT does not permanently alter the article being inspected, it is a highly valuable technique that can save both money and time in product evaluation, troubleshooting, and research. The six most frequently used NDT methods are eddy-current, magnetic-particle, liquid penetrant, radiographic, ultrasonic, and visual testing. NDT is commonly used in forensic engineering, mechanical engineering, petroleum engineering, electrical engineering, civil engineering, systems engineering, aeronautical engineering, medicine, and art. Innovations in the field of nondestructive testing have had a profound impact on medical imaging, including on echocardiography, medical ultrasonography, and digital radiography.
In electrical engineering, partial discharge (PD) is a localized dielectric breakdown (DB) of a small portion of a solid or fluid electrical insulation (EI) system under high voltage (HV) stress. While a corona discharge (CD) is usually revealed by a relatively steady glow or brush discharge (BD) in air, partial discharges within solid insulation system are not visible.
A security alarm is a system designed to detect intrusions, such as unauthorized entry, into a building or other areas, such as a home or school. Security alarms protect against burglary (theft) or property damage, as well as against intruders. Examples include personal systems, neighborhood security alerts, car alarms, and prison alarms.
Prognostics is an engineering discipline focused on predicting the time at which a system or a component will no longer perform its intended function. This lack of performance is most often a failure beyond which the system can no longer be used to meet desired performance. The predicted time then becomes the remaining useful life (RUL), which is an important concept in decision making for contingency mitigation. Prognostics predicts the future performance of a component by assessing the extent of deviation or degradation of a system from its expected normal operating conditions. The science of prognostics is based on the analysis of failure modes, detection of early signs of wear and aging, and fault conditions. An effective prognostics solution is implemented when there is sound knowledge of the failure mechanisms that are likely to cause the degradations leading to eventual failures in the system. It is therefore necessary to have initial information on the possible failures in a product. Such knowledge is important to identify the system parameters that are to be monitored. Potential uses for prognostics is in condition-based maintenance. The discipline that links studies of failure mechanisms to system lifecycle management is often referred to as prognostics and health management (PHM), sometimes also system health management (SHM) or—in transportation applications—vehicle health management (VHM) or engine health management (EHM). Technical approaches to building models in prognostics can be categorized broadly into data-driven approaches, model-based approaches, and hybrid approaches.
Reliability engineering is a sub-discipline of systems engineering that emphasizes the ability of equipment to function without failure. Reliability is defined as the probability that a product, system, or service will perform its intended function adequately for a specified period of time, OR will operate in a defined environment without failure. Reliability is closely related to availability, which is typically described as the ability of a component or system to function at a specified moment or interval of time.
Condition monitoring is the process of monitoring a parameter of condition in machinery, in order to identify a significant change which is indicative of a developing fault. It is a major component of predictive maintenance. The use of condition monitoring allows maintenance to be scheduled, or other actions to be taken to prevent consequential damages and avoid its consequences. Condition monitoring has a unique benefit in that conditions that would shorten normal lifespan can be addressed before they develop into a major failure. Condition monitoring techniques are normally used on rotating equipment, auxiliary systems and other machinery like belt-driven equipment,, while periodic inspection using non-destructive testing (NDT) techniques and fit for service (FFS) evaluation are used for static plant equipment such as steam boilers, piping and heat exchangers.
Ultrasonic testing (UT) is a family of non-destructive testing techniques based on the propagation of ultrasonic waves in the object or material tested. In most common UT applications, very short ultrasonic pulse waves with centre frequencies ranging from 0.1-15 MHz and occasionally up to 50 MHz, are transmitted into materials to detect internal flaws or to characterize materials. A common example is ultrasonic thickness measurement, which tests the thickness of the test object, for example, to monitor pipework corrosion and erosion. Ultrasonic testing is extensively used to detect flaws in welds.
Level sensors detect the level of liquids and other fluids and fluidized solids, including slurries, granular materials, and powders that exhibit an upper free surface. Substances that flow become essentially horizontal in their containers because of gravity whereas most bulk solids pile at an angle of repose to a peak. The substance to be measured can be inside a container or can be in its natural form. The level measurement can be either continuous or point values. Continuous level sensors measure level within a specified range and determine the exact amount of substance in a certain place, while point-level sensors only indicate whether the substance is above or below the sensing point. Generally the latter detect levels that are excessively high or low.
Distributed temperature sensing systems (DTS) are optoelectronic devices which measure temperatures by means of optical fibres functioning as linear sensors. Temperatures are recorded along the optical sensor cable, thus not at points, but as a continuous profile. A high accuracy of temperature determination is achieved over great distances. Typically the DTS systems can locate the temperature to a spatial resolution of 1 m with accuracy to within ±1 °C at a resolution of 0.01 °C. Measurement distances of greater than 30 km can be monitored and some specialised systems can provide even tighter spatial resolutions. Thermal changes along the optical fibre cause a local variation in the refractive index, which in turn leads to the inelastic scattering of the light propagating through it. Heat is held in the form of molecular or lattice vibrations in the material. Molecular vibrations at high frequencies (10 THz) are responsible for Raman scattering. Low frequency vibrations (10–30 GHz) cause Brillouin scattering. Energy is exchanged between the light travelling through the fibre and the material itself and cause a frequency shift in the incident light. This frequency shift can then be used to measure temperature changes along the fibre.
Brake-by-wire technology in the automotive industry is the ability to control brakes through electronic means, without a mechanical connection that transfers force to the physical braking system from a driver input apparatus such as a pedal or lever.
A gas detector is a device that detects the presence of gases in an area, often as part of a safety system. A gas detector can sound an alarm to operators in the area where the leak is occurring, giving them the opportunity to leave. This type of device is important because there are many gases that can be harmful to organic life, such as humans or animals.
Weld quality assurance is the use of technological methods and actions to test or assure the quality of welds, and secondarily to confirm the presence, location and coverage of welds. In manufacturing, welds are used to join two or more metal surfaces. Because these connections may encounter loads and fatigue during product lifetime, there is a chance they may fail if not created to proper specification.
Pipeline leak detection is used to determine if a leak has occurred in systems which contain liquids and gases. Methods of detection include hydrostatic testing, tracer-gas leak testing, infrared, laser technology, and acoustic or sonar technologies. Some technologies are used only during initial pipeline installation and commissioning, while other technologies can be used for continuous monitoring during service.
Fault detection, isolation, and recovery (FDIR) is a subfield of control engineering which concerns itself with monitoring a system, identifying when a fault has occurred, and pinpointing the type of fault and its location. Two approaches can be distinguished: A direct pattern recognition of sensor readings that indicate a fault and an analysis of the discrepancy between the sensor readings and expected values, derived from some model. In the latter case, it is typical that a fault is said to be detected if the discrepancy or residual goes above a certain threshold. It is then the task of fault isolation to categorize the type of fault and its location in the machinery. Fault detection and isolation (FDI) techniques can be broadly classified into two categories. These include model-based FDI and signal processing based FDI.
Integrated vehicle health management (IVHM) or integrated system health management (ISHM) is the unified capability of systems to assess the current or future state of the member system health and integrate that picture of system health within a framework of available resources and operational demand.
Condition monitoring of transformers in electrical engineering is the process of acquiring and processing data related to various parameters of transformers to determine their state of quality and predict their failure. This is done by observing the deviation of the transformer parameters from their expected values. Transformers are the most critical assets of electrical transmission and distribution systems, and their failures could cause power outages, personal and environmental hazards, and expensive rerouting or purchase of power from other suppliers. Identifying a transformer which is near failure can allow it to be replaced under controlled conditions at a non-critical time and avoid a system failure.
The growing demand for renewable energy has resulted in global adoption and rapid expansion of wind turbine technology. Wind Turbines are typically designed to reach a 20-year life, however, due to the complex loading and environment in which they operate wind turbines rarely operate to that age without significant repairs and extensive maintenance during that period. In order to improve the management of wind farms there is an increasing move towards preventative maintenance as opposed to scheduled and reactive maintenance to reduce downtime and lost production. This is achieved through the use of prognostic monitoring/management systems.
An intelligent maintenance system (IMS) is a system that uses collected data from machinery in order to predict and prevent potential failures in them. The occurrence of failures in machinery can be costly and even catastrophic. In order to avoid failures, there needs to be a system which analyzes the behavior of the machine and provides alarms and instructions for preventive maintenance. Analyzing the behavior of the machines has become possible by means of advanced sensors, data collection systems, data storage/transfer capabilities and data analysis tools. These are the same set of tools developed for prognostics. The aggregation of data collection, storage, transformation, analysis and decision making for smart maintenance is called an intelligent maintenance system (IMS).
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