1、Fault diagnostic systems for agricultural machineryGeert Craessaerts, Josse De Baerdemaeker, Wouter SaeysFault 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 art
2、ificial 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 p
3、rocess 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 ac
4、tions 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
5、 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 mac
6、hinery 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 suita
7、ble 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 isolati
8、on 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
9、 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 stan
10、dard techniques, suggestions for future research concerning fault diagnosis on agricultural machinery are given as well.1. IntroductionThe introduction of process control has made a remarkable contribution to the world of agricultural technology. In the past, different processes on agricultural mach
11、inery 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).
12、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 difficu
13、lt 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 safe
14、ty 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 diag
15、nosis 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
16、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 d
