Fault diagnostic systems for agricultural machinery Geert Craessaerts, Josse De Baerdemaeker, Wouter Saeys Fault detection and diagnosis in process industry have attracted a lot of attention recently. There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial and statistical methods. From a modelling perspective, the methods can rely on quantitative, semi-quantitative and qualitative models. At the other end of the spectrum, there are historical data-based methods that do not make use of any form of model information but rely only on historical process data. The basic aim of this study is to emphasize the importance of introducing more advanced multivariate fault diagnostic systems on agricultural machinery. Up till now, farmers and contractors still observe the process in order to detect process and sensor failures which can disturb the actions of the controllers and cause severe damage to the machine. In the future, the complete reliance on human operators for the correct functioning of these systems will become too risky, due to the increasing complexity of this type of machinery. A systematic and comparative study of various fault diagnostic methods, from an agricultural machinery perspective, is provided in this study. The different fault diagnostic techniques, investigated in scientific literature, are compared and evaluated on a common set of criteria. Typical requirements of a fault diagnostic system for agricultural machinery are adaptability to process changes, user-friendliness, quick detection and robustness. Based on these findings, a hybrid framework of qualitative model-based fault detection techniques and pattern recognition-based methods, which rely on historical process data, is proposed as the most suitable fault diagnostic technique. As a first step towards more advanced fault detection and isolation systems, the general applicability of intelligent neural network techniques like supervised self-organizing maps (SOMs) and back-propagation neural networks is illustrated for the detection and isolation of sensor failures on a New Holland CX combine harvester. Pattern recognition techniques, such as neural networks, were found to be very suitable for this kind of application because a lot of historical process data is available since the recent generation of combine harvesters is equipped with a wide range of sensors and actuators, which are continuously monitored. Moreover, these pattern recognition techniques allow quick detection, are easy to use and are able to adapt their structure and/or model parameters based on new measurement data. Since there is room for improvement of these standard techniques, suggestions for future research concerning fault diagnosis on agricultural machinery are given as well. 1. Introduction The introduction of process control has made a remarkable contribution to the world of agricultural technology. In the past, different processes on agricultural machinery were performed by human operators, but now the larger part is handled in an automatic manner by low and high-level control actions (Coen, Saeys, Missotten, & De Baerdemaeker, 2007; Coen, Vanrenterghem, Saeys, & De Baerdemaeker,2008; Craessaerts, Saeys, Missotten, & De Baerdemaeker, in press). At a supervisory level, human operators still observe the process in order to detect process malfunctions, abnormal events and/or sensor failures which can disturb the actions of the controllers and cause severe damage to the whole process. However, this supervisory task becomes increasingly difficult for agricultural machinery operators due to the ever increasing workload and machine complexity they have to deal with. As a result, human operators often make erroneous decisions concerning the supervisory control of these machines which can have a significant economic, environmental and/or safety impact. Operating on uncertain or missing data may cause improper control actions and consequently the system will not be operating optimally. One of the next challenges for control engineers involved with the automation of agricultural machinery will be the automation of fault detection and diagnosis to further lighten the job of the operator. In this context, a fault can be defined as a departure from an acceptable range of an observed variable or a calculated parameter associated with a process (Himmelblau, 1978). This defines a fault as a process abnormality or symptom, such as too high a pressure or too high a temperature of a hydrostatic pump. Faults can have different sources and can be classified into three classes of failures: caused by malfunctioning sensors and/or actuators, structural changes in the process or a sudden change of model parameters. The latter one is mainly caused by external disturbances whose dynamics are not taken into account in the process model. In this paper, an overview will be given of the different diagnostic techniques described in the literature for fault detection and diagnosis. Up till now, most of these techniques have been applied in the process industry because of the critical safety norms these processes deal with. It will be shown that fault diagnostic systems have not been given much attention yet in agricultural machinery research. However, these techniques could be of high value at a supervisory control level for agricultural machinery. Based on a formulation of the specific characteristics that a fault diagnostic system for agricultural machinery should include, a suggestion will be made of the most suitable diagnostic methods. Finally, the usefulness of artificial neural networks as a fault diagnostic tool for sensor failure detection will be investigated for an example case. This case study encompasses the detection and isolation of sensor failures on a New Holland CX combine harvester by means of self-organizing maps (SOMs) and back-propagation neural networks. 2. Fault detection and isolation techniques In the literature, fault diagnosis methods are broadly classified into three categories based on the type and amount of prior knowledge they use. A distinction can be made between quantitative model-based methods, qualitative model-based methods and process history-based methods (Venkatasubramanian, Rengaswamy, Yin, and Kavuri, 2003). The basic a priori knowledge that is needed for fault diagnosis is the set of possible failures and the relationship between the observations (symptoms) and the failures. This a priori domain knowledge may be derived from: - a fundamental understanding of the process using first principles models: such knowledge is referred to as causal or model-based knowledge, - historical process data: in this case, the knowledge is referred to as process history-based knowledge. The model-based a priori knowledge can be broadly classified as qualitative or quantitative. The model is usually developed based on some fundamental understanding of the physics of the process. In quantitative models this understanding is expressed in terms of a mathematical functional relationship between the inputs and outputs of the system. In contrast, in qualitative model equations these relationships are expressed in terms of heuristic functions cantered around different units in a process. An excellent review of the different fault detection and isolation (FDI) techniques discussed in scientific literature is given by Venkatasubramanian, Rengaswamy, and Kavuri (2003); Venkatasubramanian, Rengaswamy, Kavuri, and Yin (2003); Venkatasubramanian, Rengaswamy, Yin, et al. (2003). In this section, these different techniques will be briefly communicated in order to highlight the advantages and shortcomings of the discussed techniques. This critical evaluation will be based on a formulation of the desirable characteristics the ideal FDI system should possess. The conclusions drawn from this review will be of high importance for readers wishing to implement a FDI system for their particular application. 2.1. Desired characteristics of a fault diagnostic system In Venkatasubramanian, Rengaswamy, Yin, et al. (2003), an overview is given of the characteristics the ideal FDI should possess: - A quick detection and diagnosis of faults: a trade-off should be made between quick detection of faults and sensitivity to measurement noise. A high sensitivity to noise will lead to frequent false alarms during normal operation. - Isolation of faults: the fault diagnostic system should be able to make a distinction between different types of failures. - Robustness: the fault diagnostic system should be robust with respect to measurement noise and model uncertainties. - Novelty identification: the fault diagnostic system should be able to recognize the occurrence of novel faults and not misclassify these as one of the known malfunctions or as normal operation. - Classification error estimate: in order to make the system more reliable for the user, a prior estimate of the classification errors that can occur should be provided. - Adaptability: most processes in the real world are time varying because of changes in environmental conditions and/or product characteristics. The diagnostic system should be adaptable to these changes. - Explanation facility: besides the ability of the system to identify the source of malfunctioning, the diagnostic system should also provide an explanation of how the fault originated and propagated into the current situation. - Low modeling requirements: the modeling effort for the development of the diagnostic classifier should be as low as possible. - Low computational requirements: with an eye on an implementation of the diagnostic classifier on a system with fast dynamics, the implementation algorithm should be of low complexity. - Multiple fault identification: the fault diagnosis system should be able to identify multiple faults occurring at the same time. 2.2. Quantitative model-based methods In quantitative model-based FDI methods, one makes use of the inconsistencies, also called the residuals, between the actual and predicted process behavior. As a first step, the residuals between the real system response and the modeled system response are calculated. Any inconsistency, expressed as residuals, can be used for detection and isolation purposes. The residuals should be close to zero when no fault occurs, but show ‘significant’ values when the underlying system changes. In a final step, a decision algorithm will make the appropriate fault diagnosis. As mentioned above, the generation of the diagnostic residuals requires an explicit mathematical model of the system. Consequently, the complexity and reliability of the resulting FDI system depends on the kind of modeling method and comparison strategy that was used (Venkatasubramanian, Rengaswamy, Yin, et al., 2003). Either first-principles models, black-box or statistical models can be used. First-principles models are based on a physical understanding of the process and are of high complexity when dealing with supervisory control and diagnosis of a whole plant which very often has non-linear characteristics. As a result, first-principle models are seldom used for fault diagnosis. Most of the FDI methods use discrete black-box and/or statistical plant models such as input–output or state space models and assume linearity of the plant (Venkatasubramanian, Rengaswamy, Yin, et al., 2003). Process faults usually cause a change in the state variables, a change in the model parameters and/or a change in the output of the process. Based on the process model, one can estimate the non-measurable state variables or model parameters by the observed outputs and inputs using state estimation and parameter estimation methods. Typical state estimation techniques used in fault diagnosis are the Kalman filter and the Luemberger observer (Clark, 1978; Frank, 1986; Patton, Chen, & Nielsen, 1995). These reconstruct the unknown states based on the measurements or subsets of the measurement data. The Luemberger observer is typically used in a deterministic setting while the Kalman filter is mainly used for stochastic processes (Betta and Pietrosanto, 2000). As a consequence, the deviations (residuals) of the model parameters and/or state variables from the normal situation can be used as a fault indicator. Similarly, parity relations (Gertler, 1995; Willsky, 1976) check the consistency of the modeled process output with the real measured process output. Any observed inconsistency would result in a high output residual and indicate the occurrence of a typical fault. Once the residuals are calculated, they have to be evaluated. When designing the decision algorithm, a trade-off should be made between fast and reliable fault detection. In most applications of residual observation, a simple threshold function is used. However, more scientific statistical and/or neural network classifiers are preferred (Koppen-Seliger, Frank, & Wolff, 1995). When evaluating quantitative model-based fault detection systems, it should be noted that these techniques require a high modeling effort and are generally restricted to linear systems and some specific non-linear systems. For a general non-linear system, linear approximations can be poor and hence the effectiveness of the method can be greatly reduced. However, thanks to the method of disturbance decoupling, the robustness can be maximized by minimizing the effect of unknown disturbances, like measurement and process noise, and unmodelled process behavior. In this approach, all uncertainties are treated as disturbances and filters are designed to decouple the effects of faults and uncertainties such that these can be differentiated (Frank & Wunnenberg, 1989; Viswanadham & Srichander, 1987). 2.3. Qualitative model-based methods As noted above, when the a priori domain knowledge is developed from a fundamental understanding of the process by means of physical process knowledge, it is called causal model-based knowledge. When the physics of the process is expressed as mathematical functional relations between inputs, outputs and states of the system a quantitative modeling approach is used as mentioned in the previous section. When the physical relationships are expressed by means of qualitative, non-quantified functions the term qualitative modeling is used. A distinction should be made between the causal models and the abstraction hierarchies (Venkatasubramanian, Rengaswamy, & Kavuri, 2003). In a first attempt, knowledge-based expert systems, which mimic the fault detection by human experts, were investigated as a tool for fault diagnosis. However, the rule base, which consists of ‘if–then’ rules, grows rapidly with increasing complexity of the system. Another problem of this approach is the lack of insight into the physics of the system which means that it will fail when new conditions are encountered that were not defined in the rule base (Venkatasubramanian, Rengaswamy, & Kavuri, 2003). The need for a reasoning tool which can model the system in a qualitative way and describe it by a causal structure which is not as rigid as a numerical or analytical model has led to the development of different qualitative modeling methods, like digraphs and fault tree structures (Venkatasubramanian, Rengaswamy, & Kavuri, 2003). A digraph is a graph with directed arcs between the nodes which represents the cause–effect relation of a system. The directed arcs lead from the ‘cause’ nodes to the ‘effect’ nodes. As a result, it is an efficient way of representing the observed symptoms or patterns of a fault in a graphical way. Maurya, Rengeswany, and Venkatasubramanian (2007) proposed a digraph-based fault detection framework to select a possible candidate set of faults based on the incipient response of the process. Fault trees are mainly used in analyzing the system reliability and safety. The tree has different layers with nodes and at each node logic operations like AND and OR are performed for propagation. Fault trees serve to represent the propagation path of a fault from their origin to their top level of occurrence. Another way of presenting model-based knowledge is through the development of abstraction hierarchies. These are based on the decomposition of the process system into different subsystems. The main idea is to gain insight in the overall process behavior by inspection of the laws governing the different subsystems. The failure of a higher-level subsystem will be caused by the failure of one or more of the subsystems. The main source of malfunctioning can then be found by making use of a bottom-up description, which describes what various units with certain functions are used for and how these serve the higher-level systems. When evaluating qualitative model-based fault detection systems, it can be concluded that these techniques are of high value when an abundance of process experience is available which is not numerically detailed. One of the main advantages of qualitative methods based on deep-knowledge is that they provide an explanation of the path of propagation. However, their complexity will increase very rapidly with the complexity of the system and, in comparison with quantitative model-based techniques; they suffer from the resolution problem because no detailed interval or order of magnitude information is available. 2.4. Process history-based methods In contrast to the model-based fault diagnosis approaches where a process model is needed a priori, only a large amount of historical process data is needed in process history-based fault diagnosis methods. Different kinds of features are then extracted from these historical process data. The extracted features can be of qualitative and/or quantitative nature (Venkatasubramanian, Rengaswamy, Kavuri, et al., 2003). In the former case a distinction can be made between expert systems and trend modeling methods. An expert system typically consists of a set of heuristic rules derived from a knowledge base. Since considerable process knowledge is often available from experienced engineers and/or operators of the process plant, this can be incorporated. A fuzzy rule base serves as the ideal framework for the incorporation of human knowledge into a fault diagnosis system. Several authors have discussed expert system applications for fault diagnosis of specific systems (Chester, Lamb, & Dhurjati, 1984; Henley, 1984; Rich, Venkatasubramanian, Nasrallah, & Matteo, 1989). In the case of qualitative trend analysis, the different process signals are monitored and the qualitative analysis of their trends provides valuable information for the identification of underlying abnormalities in the process. These trends can be extracted from a qualitative analysis of the shape of the dynamics of a sensor signal. Venkatasubramanian, Rengaswamy, Kavuri, et al. (2003) state that a suitable classification and analysis of process trends can detect the fault earlier and lead to a quick repair of the faulty sensor. When extracting quantitative features from a historical data set, the fault diagnosis problem can be solved by pattern recognition techniques. The main goal of pattern recognition is to classify the quantitative features into different predetermined classes based on the interrelationship of these features. The number of classes equals n + 1, with n the number of faults to be isolated. An extra class is needed to cluster the data points which correspond to the normal mode of operation. These pattern recognition techniques can be broadly classified into statistical and non-statistical (neural network) ones. Traditionally used neural network classifiers are the supervised back-propagation algorithm, self-organizing maps and support vector machines. Some of them will be investigated further in detail in Section 4.2. When evaluating process his