Mechanical Fault Diagnosis And Condition Monitoring Collacott Pdf

mechanical fault diagnosis and condition monitoring collacott pdf

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Mechanical Fault Diagnosis and condition monitoring

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Log In Sign Up. Download Free PDF. A review on machinery diagnostics and prognostics implementing condition-based maintenance Mechanical Systems and Signal Processing, Dragan Banjevic. Andrew Jardine. Daming Lin. Download PDF. A short summary of this paper. A review on machinery diagnostics and prognostics implementing condition-based maintenance. Good product design is of course essential for products with high reliability.

However, no matter how good the product design is, products deteriorate over time since they are operating under certain stress or load in the real environment, often involving randomness. Maintenance has, thus, been introduced as an efficient way to assure a satisfactory level of reliability during the useful life of a physical asset.

The earliest maintenance technique is basically breakdown maintenance also called unplanned maintenance, or run-to-failure maintenance , which takes place only at breakdowns. A later maintenance technique is time-based preventive maintenance also called planned maintenance , which sets a periodic interval to perform preventive maintenance regardless of the health status of a physical asset.

With the rapid development of modern technology, products have become more and more complex while better quality and higher reliability are required. This makes the cost of preventive maintenance higher and higher. Eventually, preventive maintenance has become a major expense of many industrial companies. Therefore, more efficient maintenance approaches such as condition-based maintenance CBM are being implemented to handle the situation.

Martin [1] briefly summarised the history of maintenance technique development for machine tools. Indeed, the history applies to other types of machines and systems as well.

CBM is a maintenance program that recommends maintenance actions based on the information collected through condition monitoring. CBM attempts to avoid unnecessary maintenance tasks by taking maintenance actions only when there is evidence of abnormal behaviours of a physical asset. A CBM program, if properly established and effectively implemented, can significantly reduce maintenance cost by reducing the number of unnecessary scheduled preventive maintenance operations.

A CBM program consists of three key steps [2] see Fig. Data acquisition step information collecting , to obtain data relevant to system health. Data processing step information handling , to handle and analyse the data or signals collected in step 1 for better understanding and interpretation of the data.

Maintenance decision-making step decision-making , to recommend efficient maintenance policies. Diagnostics and prognostics are two important aspects in a CBM program. Diagnostics deals with fault detection, isolation and identification when it occurs. Fault detection is a task to indicate whether something is going wrong in the monitored system; fault isolation is a task to locate the component that is faulty; and fault identification is a task to determine the nature of the fault when it is detected.

Prognostics deals with fault prediction before it occurs. Fault prediction is a task to determine whether a fault is impending and estimate how soon and how likely a fault will occur. Diagnostics is posterior event analysis and prognostics is prior event analysis.

Prognostics is much more efficient than diagnostics to achieve zero-downtime performance. Diagnostics, however, is required when fault prediction of prognostics fails and a fault occurs. A CBM program can be used to do diagnostics or prognostics, or both. The literature on machinery diagnostics and prognostics is huge and diverse primarily due to a wide variety of systems, components and parts. Hundreds of papers in this area, including theories and practical applications, appear every year in academic journals, conference proceedings and technical reports.

This paper reviews the research on diagnostics and prognostics of mechanical systems implementing CBM with an emphasis on models, algorithms and technologies for data processing and maintenance decision-making. Some published reviews or overviews related to this topic with emphasis on specific kinds of systems or components are [1,[3][4][5][6][7][8][9][10]. The remaining part of the paper is organised as follows. Section 2 briefly describes the data acquisition step in order to accomplish diagnostics and prognostics.

Section 3 reviews models and methods for data processing that is essential to diagnostics and prognostics. Section 4 reviews the ideas and methodologies for maintenance decision-making, the final step to accomplish diagnostics and prognostics.

Finally, Section 5 concludes the paper by summarising a short list of references to provide introductory familiarity with ideas and methodologies in this area, pointing out some existing problems in diagnostics and prognostics, and addressing research directions needed for the next generation of diagnostic and prognostic systems and possible future development trends of diagnostics and prognostics.

Data acquisitionData acquisition is a process of collecting and storing useful data information from targeted physical assets for the purpose of CBM. This process is an essential step in implementing a CBM program for machinery fault or failure, which is usually caused by one or more machinery faults diagnostics and prognostics.

Data collected in a CBM program can be categorised into two main types: the so-called event data and condition monitoring data. Event data include the information on what happened e. Condition monitoring data are very versatile. It can be vibration data, acoustic data, oil analysis data, temperature, pressure, moisture, humidity, weather or environment data, etc. Various sensors, such as micro-sensors, ultrasonic sensors, acoustic emission sensors, etc.

Wireless technologies, such as Bluetooth, have provided an alternative solution to cost-effective data communication. Maintenance information systems, such as computerised maintenance management systems CMMS , enterprise resource planning systems, etc.

Collection of event data usually requires manual data entry to the information systems. With the rapid development of computer and advanced sensor technologies, data acquisition facilities and technologies have become more powerful and less expensive, making data acquisition for CBM implementation more affordable and feasible. This paper will not cover the details of data acquisition techniques. One point the authors would like to make is that event data and condition monitoring data are equally important in CBM.

In CBM practice, however, people tend to put more emphasis on the collection of the condition monitoring data and sometimes neglect the collection of event data. The overlooking of event data may result from the erroneous belief that event data are not valuable as long as the condition indicators or features seem to be working well in reducing equipment failures. This belief is incorrect since the event data are at least helpful in assessing the performance of current condition indicators or features , and can even be used either as feedback to the system designer for consideration of system redesign or improvement of condition indicators or features.

The overlooking may also result from the fact that event data collection usually requires manual data entry. Once a human is involved, everything becomes more complicated and error-prone. A solution might be to implement and automate event data collection and reporting in the maintenance information system. Data processingThe first step of data processing is data cleaning. This is an important step since data, especially event data, which is usually entered manually, always contains errors.

Data cleaning ensures, or at least increases the chance, that clean error-free data are used for further analysis and modelling. Without the data cleaning step, one may get into the so-called ''garbage in garbage out'' situation. Data errors are caused by many factors including the human factor mentioned above. For condition monitoring data, data errors may be caused by sensor faults. In this case, sensor fault isolation [14] is the right way to go.

In general, however, there is no simple way to clean data. Sometimes it requires manual examination of data. Graphical tools would be very helpful to finding and removing data errors. Data cleaning is, indeed, a big area. It is beyond the scope of this paper and will not be discussed in detail here.

The next step of data processing is data analysis. A variety of models, algorithms and tools are available in the literature to analyse data for better understanding and interpretation of data. The models, algorithms and tools used for data analysis depend mainly on the types of data collected. As mentioned above, condition monitoring data collected from the data acquisition step are versatile. It falls into three categories:Value type: Data collected at a specific time epoch for a condition monitoring variable are a single value.

For example, oil analysis data, temperature, pressure and humidity are all value type data. Waveform type: Data collected at a specific time epoch for a condition monitoring variable are a time series, which is often called time waveform. For example, vibration data and acoustic data are waveform type.

Multidimension type: Data collected at a specific time epoch for a condition monitoring variable are multidimensional.

The most common multidimensional data are image data such as infrared thermographs, X-ray images, visual images, etc. Data processing for waveform and multidimensional data is also called signal processing. Various signal processing techniques have been developed to analyse and interpret waveform and multidimensional data to extract useful information for further diagnostic and prognostic purpose.

The procedure of extracting useful information from raw signals is the so-called feature extraction. Signal processing for multidimensional data such as image processing is similar to but more complicated than waveform signal processing due to one more dimension involved. In practice, raw images are usually very complicated and immediate information for fault detection is unavailable. In these cases, image processing techniques are powerful to extract useful features from raw images for fault diagnosis-see [15,16] for descriptions and discussions on image processing tools and algorithms.

Image processing seems unnecessary when raw images provide sufficient and clear information to identify patterns and detect faults. However, image processing can still help in extracting features for automatic fault detection in such situations.

In addition to raw images obtained via data acquisition, some waveform processing techniques such as time-frequency analysis also produce images.

mechanical fault diagnosis and condition monitoring || vibration analysis

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A review on machinery diagnostics and prognostics implementing condition-based maintenance

It seems that you're in Germany. We have a dedicated site for Germany. Although the most sophisticated fault diagnosis and condition monitoring systems have their origin in the aerospace and nuclear energy industries, their use is by no means restricted to such areas of 'high technology'.

Trend Analysis in Condition Monitoring of Process Equipments

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Zhang, S. April 1, Gas Turbines Power. April ; 2 : — The objective of this paper is the development of an efficient intelligent diagnostic procedure that considers several diagnostic indices for the quantification of developing faults and for monitoring machine condition. In this procedure, the condition monitoring is performed based on the on-line vibration measurements, and further, the fault quantification is formulated into a multivariate trend analysis.

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