Structural health monitoring (SHM) involves the observation and analysis of a system over time using periodically sampled response measurements to monitor changes to the material and geometric properties of engineering structures such as bridges and buildings. [1]
In an operational environment, structures degrade with age and use. Long term SHM outputs periodically updated information regarding the ability of the structure to continue performing its intended function. After extreme events, such as earthquakes or blast loading, SHM is used for rapid condition screening. SHM is intended to provide reliable information regarding the integrity of the structure in near real time. [2]
The SHM process involves selecting the excitation methods, the sensor types, number and locations, and the data acquisition/storage/transmittal hardware commonly called health and usage monitoring systems. Measurements may be taken to either directly detect any degradation or damage that may occur to a system or indirectly by measuring the size and frequency of loads experienced to allow the state of the system to be predicted.
To directly monitor the state of a system it is necessary to identify features in the acquired data that allows one to distinguish between the undamaged and damaged structure. One of the most common feature extraction methods is based on correlating measured system response quantities, such a vibration amplitude or frequency, with observations of the degraded system. Damage accumulation testing, during which significant structural components of the system under study are degraded by subjecting them to realistic loading conditions, can also be used to identify appropriate features. This process may involve induced-damage testing, fatigue testing, corrosion growth, or temperature cycling to accumulate certain types of damage in an accelerated fashion.
Qualitative and non-continuous methods have long been used to evaluate structures for their capacity to serve their intended purpose. Since the beginning of the 19th century, railroad wheel-tappers have used the sound of a hammer striking the train wheel to evaluate if damage was present. In rotating machinery, vibration monitoring has been used for decades as a performance evaluation technique. [2] Two techniques in the field of SHM are wave propagation based techniques [3] and vibration based techniques. [4] [5] [6] Broadly the literature for vibration based SHM can be divided into two aspects, the first wherein models are proposed for the damage to determine the dynamic characteristics, also known as the direct problem, and the second, wherein the dynamic characteristics are used to determine damage characteristics, also known as the inverse problem.
Several fundamental axioms, or general principles, have emerged: [7]
SHM System's elements typically include:
An example of this technology is embedding sensors in structures like bridges and aircraft. These sensors provide real time monitoring of various structural changes like stress and strain. In the case of civil engineering structures, the data provided by the sensors is usually transmitted to a remote data acquisition centres. With the aid of modern technology, real time control of structures (Active Structural Control) based on the information of sensors is possible
Commonly known as Structural Health Assessment (SHA) or SHM, this concept is widely applied to various forms of infrastructures, especially as countries all over the world enter into an even greater period of construction of various infrastructures ranging from bridges to skyscrapers. Especially so when damages to structures are concerned, it is important to note that there are stages of increasing difficulty that require the knowledge of previous stages, namely:
It is necessary to employ signal processing and statistical classification to convert sensor data on the infrastructural health status into damage info for assessment.
Operational evaluation attempts to answer four questions regarding the implementation of a damage identification capability:
Operational evaluation begins to set the limitations on what will be monitored and how the monitoring will be accomplished. This evaluation starts to tailor the damage identification process to features that are unique to the system being monitored and tries to take advantage of unique features of the damage that is to be detected.
The data acquisition portion of the SHM process involves selecting the excitation methods, the sensor types, number and locations, and the data acquisition/storage/transmittal hardware. Again, this process will be application specific. Economic considerations will play a major role in making these decisions. The intervals at which data should be collected is another consideration that must be addressed.
Because data can be measured under varying conditions, the ability to normalize the data becomes very important to the damage identification process. As it applies to SHM, data normalization is the process of separating changes in sensor reading caused by damage from those caused by varying operational and environmental conditions. One of the most common procedures is to normalize the measured responses by the measured inputs. When environmental or operational variability is an issue, the need can arise to normalize the data in some temporal fashion to facilitate the comparison of data measured at similar times of an environmental or operational cycle. Sources of variability in the data acquisition process and with the system being monitored need to be identified and minimized to the extent possible. In general, not all sources of variability can be eliminated. Therefore, it is necessary to make the appropriate measurements such that these sources can be statistically quantified. Variability can arise from changing environmental and test conditions, changes in the data reduction process, and unit-to-unit inconsistencies.
Data cleansing is the process of selectively choosing data to pass on to or reject from the feature selection process. The data cleansing process is usually based on knowledge gained by individuals directly involved with the data acquisition. As an example, an inspection of the test setup may reveal that a sensor was loosely mounted and, hence, based on the judgment of the individuals performing the measurement, this set of data or the data from that particular sensor may be selectively deleted from the feature selection process. Signal processing techniques such as filtering and re-sampling can also be thought of as data cleansing procedures.
Finally, the data acquisition, normalization, and cleansing portion of SHM process should not be static. Insight gained from the feature selection process and the statistical model development process will provide information regarding changes that can improve the data acquisition process.
The area of the SHM process that receives the most attention in the technical literature is the identification of data features that allows one to distinguish between the undamaged and damaged structure. Inherent in this feature selection process is the condensation of the data. The best features for damage identification are, again, application specific.
One of the most common feature extraction methods is based on correlating measured system response quantities, such a vibration amplitude or frequency, with the first-hand observations of the degrading system. Another method of developing features for damage identification is to apply engineered flaws, similar to ones expected in actual operating conditions, to systems and develop an initial understanding of the parameters that are sensitive to the expected damage. The flawed system can also be used to validate that the diagnostic measurements are sensitive enough to distinguish between features identified from the undamaged and damaged system. The use of analytical tools such as experimentally-validated finite element models can be a great asset in this process. In many cases the analytical tools are used to perform numerical experiments where the flaws are introduced through computer simulation. Damage accumulation testing, during which significant structural components of the system under study are degraded by subjecting them to realistic loading conditions, can also be used to identify appropriate features. This process may involve induced-damage testing, fatigue testing, corrosion growth, or temperature cycling to accumulate certain types of damage in an accelerated fashion. Insight into the appropriate features can be gained from several types of analytical and experimental studies as described above and is usually the result of information obtained from some combination of these studies.
The operational implementation and diagnostic measurement technologies needed to perform SHM produce more data than traditional uses of structural dynamics information. A condensation of the data is advantageous and necessary when comparisons of many feature sets obtained over the lifetime of the structure are envisioned. Also, because data will be acquired from a structure over an extended period of time and in an operational environment, robust data reduction techniques must be developed to retain feature sensitivity to the structural changes of interest in the presence of environmental and operational variability. To further aid in the extraction and recording of quality data needed to perform SHM, the statistical significance of the features should be characterized and used in the condensation process.
The portion of the SHM process that has received the least attention in the technical literature is the development of statistical models for discrimination between features from the undamaged and damaged structures. Statistical model development is concerned with the implementation of the algorithms that operate on the extracted features to quantify the damage state of the structure. The algorithms used in statistical model development usually fall into three categories. When data are available from both the undamaged and damaged structure, the statistical pattern recognition algorithms fall into the general classification category, commonly referred to as supervised learning. Group classification and regression analysis are categories of supervised learning algorithms. Unsupervised learning refers to algorithms that are applied to data not containing examples from the damaged structure. Outlier or novelty detection is the primary class of algorithms applied in unsupervised learning applications. All of the algorithms analyze statistical distributions of the measured or derived features to enhance the damage identification process.
Health monitoring of large bridges can be performed by simultaneous measurement of loads on the bridge and effects of these loads. It typically includes monitoring of:
Provided with this knowledge, the engineer can:
The state of Oregon in the United States, Department of Transportation Bridge Engineering Department has developed and implemented a Structural Health Monitoring (SHM) program as referenced in this technical paper by Steven Lovejoy, Senior Engineer. [8]
References are available that provide an introduction to the application of fiber optic sensors to Structural Health Monitoring on bridges. [9]
The following projects are currently known as some of the biggest on-going bridge monitoring
An accelerometer is a device that measures the proper acceleration of an object. Proper acceleration is the acceleration of the object relative to an observer who is in free fall. Proper acceleration is different from coordinate acceleration, which is acceleration with respect to a given coordinate system, which may or may not be accelerating. For example, an accelerometer at rest on the surface of the Earth will measure an acceleration due to Earth's gravity straight upwards of about g ≈ 9.81 m/s2. By contrast, an accelerometer that is in free fall will measure zero acceleration.
A strain gauge is a device used to measure strain on an object. Invented by Edward E. Simmons and Arthur C. Ruge in 1938, the most common type of strain gauge consists of an insulating flexible backing which supports a metallic foil pattern. The gauge is attached to the object by a suitable adhesive, such as cyanoacrylate. As the object is deformed, the foil is deformed, causing its electrical resistance to change. This resistance change, usually measured using a Wheatstone bridge, is related to the strain by the quantity known as the gauge factor.
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.
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.
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 promises 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.
Modal analysis is the study of the dynamic properties of systems in the frequency domain. It consists of mechanically exciting a studied component in such a way to target the modeshapes of the structure, and recording the vibration data with a network of sensors. Examples would include measuring the vibration of a car's body when it is attached to a shaker, or the noise pattern in a room when excited by a loudspeaker.
Deformation monitoring is the systematic measurement and tracking of the alteration in the shape or dimensions of an object as a result of stresses induced by applied loads. Deformation monitoring is a major component of logging measured values that may be used for further computation, deformation analysis, predictive maintenance, and alarming.
Civionics is the combination of civil engineering with electronics engineering, in a manner similar to avionics and mechatronics. An emerging discipline, the main application area of civionics is currently the use of electronics for structural health monitoring (SHM) of civil structures, particularly photonics.
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.
Brüel & Kjær is a Danish multinational engineering and electronics company headquartered in Nærum, near Copenhagen. It was the largest producer in the world of equipment for acoustic and vibrational measurements. Brüel & Kjær is a subsidiary of Spectris.
In geophysics, geology, civil engineering, and related disciplines, seismic noise is a generic name for a relatively persistent vibration of the ground, due to a multitude of causes, that is often a non-interpretable or unwanted component of signals recorded by seismometers.
FEMtools is a multi-functional, cross-platform and solver-independent family of CAE software programs providing analysis and scripting solutions for many different types of engineering simulation applications. The program is developed, supported and licensed by Dynamic Design Solutions ("DDS") NV, located in Leuven, Belgium.
The Illinois Structural Health Monitoring Project (ISHMP) is a structural health monitoring project devoted to researching and developing hardware and software systems to be used for distributed real-time monitoring of civil infrastructure. The project focuses on monitoring bridges, and aims to reduce the cost and installation effort of structural health monitoring equipment. It was founded in 2002 by Professor Bill F. Spencer and Professor Gul Agha of the University of Illinois at Urbana–Champaign.
Operational loads monitoring (OLM) is a term given to act of investigating the characteristics of a structure in its normal operating environment. This term is often used to describe programs involving aircraft to extending their in-service life in a manner that does not compromise flight safety. A typical program would involve the installation of strain gauges to measure loads, accelerometers to measure g-force and other parameters to support the program or to add value, data acquisition system to process this data and a recorder to save the data for later analysis . In this way it is very similar to structural health monitoring, a term that is sometimes also used to describe operational loads monitoring. Unlike Health and Usage Monitoring Systems, OLM programs are generally a short term project used to assess the remaining useful safe life of an airframe. This is especially important when an aircraft's role changes as the stresses and strains may now be significantly different from those initially anticipated. OLM program's benefits include a possible increased safe operating life figure and helping to prevent accidents such as the C-130 crash that occurred after the platform had been modified and flown for a different mission . There are several active OLM programs currently underway, including research initiatives to standardize approaches for civilian aircraft.
A shock data logger or vibration data logger is a measurement instrument that is capable of autonomously recording shocks or vibrations over a defined period of time. Digital data is usually in the form of acceleration and time. The shock and vibration data can be retrieved, viewed and evaluated after it has been recorded.
The term "smart structures" is commonly used for structures which have the ability to adapt to environmental conditions according to the design requirements. As a rule, the adjustments are designed and performed in order to increase the efficiency or safety of the structure. Combining "smart structures" with the "sophistication" achieved in materials science, information technology, measurement science, sensors, actuators, signal processing, nanotechnology, cybernetics, artificial intelligence, and biomimetics, one can talk about Smart Intelligent Structures. In other words, structures which are able to sense their environment, self-diagnose their condition and adapt in such a way so as to make the design more useful and efficient.
Unified framework is a general formulation which yields nth - order expressions giving mode shapes and natural frequencies for damaged elastic structures such as rods, beams, plates, and shells. The formulation is applicable to structures with any shape of damage or those having more than one area of damage. The formulation uses the geometric definition of the discontinuity at the damage location and perturbation to modes and natural frequencies of the undamaged structure to determine the mode shapes and natural frequencies of the damaged structure. The geometric discontinuity at the damage location manifests itself in terms of discontinuities in the cross-sectional properties, such as the depth of the structure, the cross-sectional area or the area moment of inertia. The change in cross-sectional properties in turn affects the stiffness and mass distribution. Considering the geometric discontinuity along with the perturbation of modes and natural frequencies, the initial homogeneous differential equation with nonconstant coefficients is changed to a series of non-homogeneous differential equations with constant coefficients. Solutions of this series of differential equations is obtained in this framework.
Ambient modal identification, also known as operational modal analysis (OMA), aims at identifying the modal properties of a structure based on vibration data collected when the structure is under its operating conditions, i.e., no initial excitation or known artificial excitation. The modal properties of a structure include primarily the natural frequencies, damping ratios and mode shapes. In an ambient vibration test the subject structure can be under a variety of excitation sources which are not measured but are assumed to be 'broadband random'. The latter is a notion that one needs to apply when developing an ambient identification method. The specific assumptions vary from one method to another. Regardless of the method used, however, proper modal identification requires that the spectral characteristics of the measured response reflect the properties of the modes rather than those of the excitation.
Bayesian operational modal analysis (BAYOMA) adopts a Bayesian system identification approach for operational modal analysis (OMA). Operational modal analysis aims at identifying the modal properties (natural frequencies, damping ratios, mode shapes, etc.) of a constructed structure using only its (output) vibration response (e.g., velocity, acceleration) measured under operating conditions. The (input) excitations to the structure are not measured but are assumed to be 'ambient' ('broadband random'). In a Bayesian context, the set of modal parameters are viewed as uncertain parameters or random variables whose probability distribution is updated from the prior distribution (before data) to the posterior distribution (after data). The peak(s) of the posterior distribution represents the most probable value(s) (MPV) suggested by the data, while the spread of the distribution around the MPV reflects the remaining uncertainty of the parameters.
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