Outline of a fault diagnosis system for a large-scale board machine

Global competition forces process industries to continuously optimize plant operation. One of the latest trends for efficiency and plant availability improvement is to set up fault diagnosis and maintenance systems for online industrial use. This paper presents a methodology for developing industrial fault detection and diagnosis (FDD) systems. Since model or data-based diagnosis of all components cannot be achieved online on a large-scale basis, the focus must be narrowed down to the most likely faulty components responsible for abnormal process behavior. One of the key elements here is fault analysis. The paper describes and briefly discusses also other development phases, process decomposition, and the selection of FDD methods. The paper ends with an FDD case study of a large-scale industrial board machine including a description of the fault analysis and FDD algorithms for the resulting focus areas. Finally, the testing and validation results are presented and discussed.Peer reviewe

Stiction Quantification: A Robust Methodology for Valve Monitoring and Maintenance Scheduling

Valve stiction is one of the most common causes of poor performance in control loops. This paper presents a procedure which allows stiction quantification. The technique permits one to estimate the unknown real stem position, and moreover, it does not need any process knowledge and requires only the data normally registered in industrial plants. It is pointed out that the real problem consists of the lack of knowledge about the true value of stiction. A general methodology is proposed to discard data for which quantification is very likely to give wrong indications and to restrict its application to appropriate cases. Simulations show that several sources of perturbations can be eliminated, thus improving the reliability of stiction evaluation. Results are confirmed by application to industrial data: a significant number of valves are analyzed for repeated acquisitions before and after plant shutdown. The proposed procedure seems to be a valid methodology to monitor valve stiction and to schedule and check valve maintenance

System identification applied to stiction quantification in industrial control loops: A comparative study

A comparative study of different models and identification techniques applied to the quantification of valve stiction in industrial control loops is presented in this paper, with the objective of taking into account for the presence of external disturbances. A Hammerstein system is used to model the controlled process (linear block) and the sticky valve (nonlinear block): five different candidates for the linear block and two different candidates for the nonlinear block are evaluated and compared. Two of the five linear models include a nonstationary disturbance term that is estimated along with the input-to-output model, and these extended models are meant to cope with situations in which significant nonzero mean disturbances affect the collected data. The comparison of the different models and identification methods is carried out thoroughly in three steps: simulation, application to pilot plant data and application to industrial loops. In the first two cases (simulation and pilot plant) the specific source of fault (stiction with/without external disturbances) is known and hence a validation of each candidate can be carried out more easily. Nonetheless, each fault case considered in the previous two steps has been found in the application to a large number of datasets collected from industrial loops, and hence the merits and limitations of each candidate have been confirmed. As a result of this study, extended models are proved to be effective when large, time varying disturbances affect the system, whereas conventional (stationary) noise models are more effective elsewhere

Stiction of surface micromachined structures after rinsing and drying: model and investigation of adhesion mechanisms

The mechanisms causing stiction of polysilicon structures fabricated by surface micromachining techniques have been investigated. It is found that during drying from rinse liquids attractive dynamic capillary forces are responsible for bringing micromechanical structures into contact with the underlying substrate. Measured adhesion energies of sticking microbridges indicate that van der Waals forces are responsible for the stiction of hydrophobic surfaces and that hydrogen bridging is an additional adhesion mechanism for hydrophilic surfaces. Methods to reduce the stiction problem are indicated

Identification techniques for stiction quantification in the presence of nonstationary disturbances

The paper presents a detailed comparison of different identification techniques applied to valve stiction quantification, possibly in the presence of nonstationary unknown disturbances. The control loop with sticky valve is modeled as a Hammerstein system, in which the nonlinearity is identified using enumeration of the parameters’ space. Five different techniques for identification of the linear model are compared in terms of achievable performance. In particular, the capability to cope with the presence of nonstationary disturbances is analyzed. The techniques allow one to estimate the unknown actual valve position (MV), without requiring any process knowledge, being based only on data which are usually recorded in industrial plants: controller output (OP) and controlled variable (PV). Simulations show that external perturbations can be tolerated, thus ensuring a reliable evaluation of stiction in practical situations where external disturbances are usually present. Models which incorporate a time varying additive nonstationary disturbance grant a better process identification and a more accurate stiction estimation in the case of disturbance acting simultaneously with valve stiction. However, simpler models are the best choice when stiction happens to be the only source of loop oscillation. Results are confirmed by application to real data: pilot plant data are used to corroborate the effectiveness of the techniques

Root cause isolation of propagated oscillations in process plants

Persistent whole-plant disturbances can have an especially large impact on product quality and running costs. There is thus a motivation for the automated detection of a plant-wide disturbance and for the isolation of its sources. Oscillations increase variability and can prevent a plant from operating close to optimal constraints. They can also camouflage other behaviour that may need attention such as upsets due to external disturbances. A large petrochemical plant may have a 1000 or more control loops and indicators, so a key requirement of an industrial control engineer is for an automated means to detect and isolate the root cause of these oscillations so that maintenance effort can be directed efficiently. The propagation model that is proposed is represented by a log-ratio plot, which is shown to be ‘bell’ shaped in most industrial situations. Theoretical and practical issues are addressed to derive guidelines for determining the cut-off frequencies of the ‘bell’ from data sets requiring little knowledge of the plant schematic and controller settings. The alternative method for isolation is based on the bispectrum and makes explicit use of this model representation. A comparison is then made with other techniques. These techniques include nonlinear time series analysis tools like Correlation dimension and maximal Lyapunov Exponent and a new interpretation of the Spectral ICA method, which is proposed to accommodate our revised understanding of harmonic propagation. Both simulated and real plant data are used to test the proposed approaches. Results demonstrate and compare their ability to detect and isolate the root cause of whole plant oscillations. Being based on higher order statistics (HOS), the bispectrum also provides a means to detect nonlinearity when oscillatory measurement records exist in process systems. Its comparison with previous HOS based nonlinearity detection method is made and the bispectrum-based is preferred

Detection And Identification Of Stiction In Control Valves Based On Fuzzy Clustering Method

Kehadiran geseran statik (stiction) dalam injap kawalan mengubah posisi injap daripada kedudukan asalnya dan menghasilkan perlakuan tak lelurus (jalur mati beserta jalur “stick” dan lompatan gelincir) dalam gelung kawalan. Ketaklelurusan ini memaksa gelung kawalan untuk berayun. Ayunan ini seterusnya menghasilkan kualiti produk yang rendah dan peningkatan dalam penggunaan tenaga. Oleh itu, pengesanan stiction pada masa yang tepat dalam gelung kawalan aliran, yang merupakan gelung kawalan penting dalam industri, adalah amat penting. Dalam kaedah-kaedah sebelum ini, apabila keujudan stiction telah dikesan, parameter stiction perlu dianggarkan (kuantifikasi) sebagai satu langkah untuk mengatasi masalah stiction. Walau bagaimanapun, penganggaran ini memerlukan pengorbanan dari segi masa dan usaha serta merupakan satu tugas yang mencabar. Dalam penyelidikan ini, untuk memperbaiki anggaran kovarians penggugusan kabur, sekaitan lelurus data adalah dikesan. Kemudian, satu matriks (yang mengandungi satu jujukan nombor rawak tak tersekait secara bersiri dengan min sifar dan varians terhingga) ditambah kepada matriks kovarians. Pengubahsuaian ini mengelakkan algoritma penggugusan kabur daripada menghadapi masalah berangka. Satu kaedah yang terhasil daripada ide bahawa dengan kewujudan stiction, akan menyebabkan pusat-pusat gugusan kawasan utama gelung kawalan aliran tersimpang daripada pusatnya, telah dicadangkan untuk mengesan sisihan (pengesanan). Selain itu, berdasarkan ide bahawa kecerunan garisan-garisan yang diperolehi daripada turutan pusat gugusan berkongsi beberapa sifat (dengan kehadiran stiction), satu indeks prestasi baru yang mengumpul sifat-sifat ini untuk membezakan punca ayunan (diagnosis) telah juga dicadangkan. Akhir sekali, sebagai alternatif kepada pengkuantitian stiction, satu model proses yang sesuai dengan stiction injap kawalan telah ditentukan (identifikasi) dengan mengkonfigurasi pengesan kabur. Model ini berupaya untuk menangkap (mengenal) semua dinamik yang bersesuaian bagi proses yang mengandungi stiction injap kawalan. Bilangan pengesanan betul yang diperolehi ialah 85%. Bukan sahaja masa pengesanan telah dapat dikurangkan kepada kurang daripada 1 saat (masa purata ialah 0.4505 saat), bahkan prestasi kaedah yang dicadangkan bagi pengesanan, diagnosis dan pengenalan stiction telah turut disahkan oleh kedua-dua data simulasi dan industri. ________________________________________________________________________________________________________________________ The presence of static friction (stiction) in control valves deviates the valve position from its origin and therefore produces a nonlinear behavior (dead band plus stick band and slip jump) in control loops. The nonlinearity forces control loops to oscillate. The oscillation results in poor product quality and increased energy consumption. The detection of stiction for flow control loops which form significant control loops in industry in a timely manner is of great importance. After the presence of stiction has been detected, in order to mitigate stiction problem, it is necessary to estimate stiction parameters (quantification) in the earlier methods. However, this estimation which requires huge investment of time and effort is a challenging task. In this study, in order to improve covariance estimation of fuzzy clustering, linearly correlation of data is detected. Then a matrix (which contains a sequence of serially uncorrelated random numbers with zero mean and finite variance) is added to covariance matrix. This modification prevents the fuzzy clustering algorithm from turning into numerical problem. A method, which gain benefits from the idea that in the presence of stiction, the cluster centers of main regions of flow control loops are deviated from their origin, is proposed to detect the deviation (detection). Furthermore, based on the idea that, the slopes of the lines obtained from successive cluster centers, share some properties (in the presence of stiction), a new performance index which collects these properties to distinguish cause of oscillation (diagnosis) is proposed. Finally as an alternative to stiction quantification, by configuring a fuzzy identifier, an appropriate model of process with control valve stiction is identified (identification). The identified model is able to capture (identify) all relevant dynamics of the process with control valve stiction. The number of correct detections is now 85%. Not only has the identification time been decreased to less than a second (i.e. average is 0.4505 seconds), the performance of the proposed methods of stiction detection, diagnosis and identification has also been confirmed by both simulation and industrial data