Introduction

Performance assessment and quality control of complex industrial process systems are of ever increasing importance in the chemical and general manufacturing industries as well as the building and construction industry (Gosselin and Ruel 2007; Marcon et al. 2005; Miletic et al. 2004; Nimmo 1995). Besides other reasons, the main drivers of this trend are: the ever more stringent legislation based on process safety, emissions and environmental pollution (ecological awareness); an increase in global competition; and the desire of companies to present a green image of their production processes and products.

Associated tasks entail the on-line monitoring of production facilities, individual processing units and systems (products) in civil, mechanical, automotive, electrical and electronic engineering. Examples of such systems include the automotive and the aerospace industries for monitoring operating conditions and emissions of internal combustion and jet engines; buildings for monitoring the energy consumption and heat loss; and bridges for monitoring stress, strain and temperature levels and hence assess elastic deformation.

To address the need for rigorous process monitoring, the level of instrumentation of processing units and general engineering systems, along with the accuracy of the sensor readings, have consequently increased over the past few decades. The information that is routinely collected and stored, for example in distributed control systems for chemical production facilities and the engine management system for internal combustion engines, is then benchmarked against conditions that are characterized as normal and/or optimal.

The data records therefore typically include a significant number of process variables that are frequently sampled. This, in turn, creates huge amounts of process data, which must be analyzed online or archived for subsequent analysis. Examples are reported for:

• the chemical industry (Al-Ghazzawi and Lennox 2008; MacGregor et al. 1991; Piovoso and Kosanovich 1992; Simoglou et al. 2000; Wang et al. 2003);
• the general manufacturing industry (Kenney et al. 2002; Lane et al. 2003; Martin et al. 2002; Monostori and Prohaszka 1993; Qin et al. 2006);
• internal combustion engines (Gérard et al. 2007; Howlett et al. 1999; Kwon et al. 1987; McDowell et al. 2008; Wang et al. 2008);
• aircraft systems (Abbott and Person 1991; Boller 2000; Jaw 2005; Jaw and Mattingly 2008; Tumer and Bajwa 1999); and
• civil engineering systems (Akbari et al. 2005; Doebling et al. 1996; Ko and Ni 2005; Pfafferott et al. 2004; Westergren et al. 1999).

For the chemical and manufacturing industries, the size of the data records and the ever increasing complexity of such systems have caused efficient process monitoring by plant operators to become a difficult task. This complexity stems from increasing levels of process optimization and intensification, which gives rise to operating conditions that are at the limits of operational constraints and which yield complex dynamic behavior (Schmidt-Traub and Górak 2006). A consequence of these trends is a reduced safety margin if the process shows some degree of abnormality, for example caused by a fault (Schuler 2006).

Examples for monitoring technical systems include internal combustion engines and gearbox systems. Process monitoring of internal combustion engines relates to tackling increasing levels of pollution caused by the emissions of an ever growing number of registered vehicles and has resulted in the introduction of the first on-board-diagnostic (OBD) system in the United States in 1988, and in Europe (EURO1) in 1992. The requirement for more advanced monitoring systems culminated in the introduction of OBDII (1994), EURO2 (1997) and EURO3 (2000) legislation. This trend has the aim of continuously decreasing emissions and is supported through further regulations, which relate to the introduction of OBDIII (considered since 2000), EURO4 (2006) and EURO5 (2009) systems.

Current and future regulations demand strict monitoring of engine performance at certain intervals under steady-state operating conditions. This task entails the diagnosis of any fault condition that could potentially cause the emissions to violate legislated values at the earliest opportunity. With respect to this development, a prediction by Powers and Nicastri (1999) indicated that the integration of model-based control systems and design techniques have the potential to produce safer, more comfortable and manoeuvrable vehicles. According to Kiencke and Nielsen (2000), there are a total of three main objectives that automotive control systems have to adhere to: (i) maintaining efficiency and low fuel consumption, (ii) producing low emissions to protect the environment and (iii) ensuring safety. Additional benefits of condition monitoring are improved reliability and economic operation (Isermann and Ballé 1997) through early fault detection.

For gearbox systems, the early detection of incipient fault conditions is of fundamental importance for their operation. Gearboxes can be found in aerospace, civil and general mechanical systems. The consequences of not being able to detect such faults at early stages can, for example, include reduced productivity in manufacturing processes, reduced efficiency of engines, equipment damage or even failure. Early detection of such faults can therefore provide significant improvements in the reduction of operational and maintenance costs, system down-time, and lead to increased levels of safety, which is of ever growing importance. An incipiently developing fault in a mechanical system usually affects certain parameters, such as vibration, noise and temperature. The analysis of these external variables therefore allows the monitoring of internal components, such as gears, which are usually inaccessible without the dismantling of the system. It is consequently essential to extract relevant information from the recorded signals with the aim of detecting any irregularities that could be caused by such faults.

The research community has utilized a number of different approaches to monitor complex technical systems. These include model-based approaches (Ding 2008; Frank et al. 2000; Isermann 2006; Simani et al. 2002; Venkatasubramanian et al. 2003) that address a wide spectrum of application areas, signal-based approaches (Bardou and Sidahmed 1994; Chen et al. 1995; Hu et al. 2003; Kim and Parlos 2003) which are mainly applied to mechanical systems, rule-based techniques (Iserman 1993; Kramer and Palowitch 1987; Shin and Lee 1995; Upadhyaya et al. 2003) and more recently knowledge-based techniques (Lehane et al. 1998; Ming et al. 1998; Qing and Zhihan 2004; Shing and Chee 2004) that blend heuristic knowledge into monitoring application. Such techniques have shown their potential whenever cost-benefit economics have justified the required effort in developing applications.

Given the characteristics of modern production and other technical systems, however, such complex technical processes may present a large number of recorded variables that are affected by a few common trends, which may render these techniques difficult to implement in practice. Moreover, such processes often operate under steady-state operation conditions that may or may not be predefined. To some extent, this also applies to automotive systems as routine technical inspections, for example once per year, usually include emission tests that are carried out at a reference steady state operation condition of the engine.

Underlying trends are, for example, resulting from known or unknown disturbances, interactions of the control system with the technical system, and minor operator interventions. This produces the often observed high degree of correlated among the recorded process variables that mainly describe common trends or common cause variation. The sampled data has therefore embedded within it information for revealing the current state of process operation. The difficult issue here is to extract this information from the data and to present it in a way that can be easily interpreted.

Based on the early work on quality control and monitoring (Hotelling 1947; Jackson 1959, 1980; Jackson and Morris 1956, 1957; Jackson and Mudholkar 1979), several research articles around the 1990s proposed a multivariate extension to statistical process control Kresta et al. (1989, 1991) MacGregor et al. (1991) Wise et al. (1989b, 1991) to generate a statistical fingerprint of a technical system based on recorded reference data. Methods that are related to this extension are collectively referred to as multivariate statistical process control or MSPC. The application of MSPC predominantly focussed on the chemical industry (Kosanovich and Piovoso 1991; Morud 1996; Nomikos and MacGregor 1994; Piovoso and Kosanovich 1992; Piovoso et al. 1991) but was later extended to general manufacturing areas (Bissessur et al. 1999; 2000; Lane et al. 2003; Martin et al. 2002; Wikström et al. 1998).

Including this earlier work, the last two decades have seen the development and application of MSPC gaining substantial interest in academe and industry alike. The recipe for the considerable interest in MSPC lies in its simplicity and adaptability for developing monitoring applications, particularly for larger numbers of recorded variables. In fact, MSPC relies on relatively few assumptions and only requires routinely collected operating data from the process to be monitored. The first of four parts of this book outlines and describes these assumptions, and is divided into a motivation for MSPC, a description of the main MSPC modeling methods and the underlying data structures, and the construction of charts to carry out on-line monitoring.

For monitoring processes in the chemical industry, the research community has proposed two different MSPC approaches. The first one relates to processes that produce a specific product on a continuous basis, i.e. they convert a constant stream of inputs into a constant stream of outputs and are referred to as a continuous processes. Typical examples of continuous processes can be found in the petrochemical industry. The second approach has been designed to monitor processes that convert a discontinuous feed into the required product over a longer period of time. More precisely, and different from a continuous process, this type of process receives a feed that remains in the reactor over a significantly longer period of time before the actual production process is completed. Examples of the second type of process can be found in the pharmaceutical industry and such processes are referred to as batch processes. This book focuses on continuous processes to provide a wide coverage of processes in different industries. References that discuss the monitoring of batch processes include Chen and Liu (2004), Lennox et al. (2001), Nomikos and MacGregor (1994, 1995), van Sprang et al. (2002) to name only a few.

The second part of this book then presents two application studies of a chemical reaction process and a distillation process. Both applications demonstrate the ease of utilizing MSPC for process monitoring and detecting as well as diagnosing abnormal process behavior. The detection is essentially a boolean decision whether current process behavior still matches the statistical fingerprint describing behavior that is deemed normal and/or optimal. If it matches, the process is in-statistical-control and if it does not the process is out-of-statistical-control. The diagnosis of abnormal events entails the identification and analysis of potential root causes that have led to the anomalous behavior. In other words, it assesses why the current plant behavior deviates from that manifested in the statistical fingerprint, constructed from a historic data record, that characterizes normal process behavior. The second part of this book also demonstrates that the groundwork on MSPC in the early to mid 1990s may rely on oversimplified assumptions that may not represent true process behavior.

The aim of the third part is then to show advances in MSPC which the research literature has proposed over the past decade in order to overcome some of the pitfalls of this earlier work. These advances include:

• improved data structures for MSPC monitoring models;
• the removal of the assumption that the stochastic process variables have a constant mean and variance, and the variable interrelationships are constant over time; and
• a fresh look at constructing MSPC monitoring charts, resulting in the introduction of a new paradigm which significantly improves the sensitivity of the monitoring scheme in detecting incipient fault conditions.

In order to demonstrate the practical usefulness of these improvements, the application studies of the chemical reactor and the distillation processes in the second part of this book are revisited. In addition, the benefits of the adaptive MSPC scheme is also shown using recorded data from a furnace process and the enhanced monitoring scheme is applied to recorded data from gearbox systems.

Finally, the fourth part of this book presents a detailed treatment of the core MSPC modeling methods, including their objective functions, and their statistical and geometric properties. The analysis also includes the discussion of computational issues in order to obtain data models efficiently.