# Application of a Selected Pseudorandom Number Generator for the Reliability of Farm Tractors
## 1 Introduction  
Agricultural equipment is a specific group due to its demanding working environment and specific usage [1]. Agricultural machines are characterized by great diversity, as they are operated in particularly harsh conditions (they are exposed to aggressive media, such as manure, fertilizers, pesticides or abrasion and cohesion; their effectiveness in the field is determined, for example, by the adhesion of the surfaces of tools working in the soil). They work mainly outdoors (rarely in farm buildings), sometimes on slopes, and their use is seasonal. Agricultural machinery is also characterized by low utilization of their potential working time, while requiring expensive storage, which is logistically difficult. Long operating cycles require regular maintenance according to manufacturer guidelines. Users require that such machines have high reliability, that technological processes are not disrupted by service interruptions, and that they guarantee low future value loss. The operation of agricultural appliances and vehicles is a continuous process, which is discrete in states. Research fields dealing with the reliability of farm machinery including tractors [2, 3, 4, 5] as well as other appliances are addressed more frequently in the scientific literature [6, 7]. Reliability is a measurable and quantitative parameter, which represents the probability that failure will not occur within the assumed period of time. Various techniques are used in order to obtain demanded data and publish reliability rankings [8]. The most popular are graphical methods [9, 10, 11, 12], fault tree analysis [13, 14, 15, 16, 17],probability distributions and models [18], as well as applications of machine learning and reliability estimations [8]. The multistate approach to the reliability of agricultural devices can be based on Markov chains, which allow for intermediate states such as a state of partial operability or a state of incomplete task operability [19; 20; 21]. The reliability of farm tractors can also be estimated with the Bernoulli competing risks or Kaplan-Meier methods [3; 22]. The classical theory of reliability has developed a wide range of functional and numerical characteristics, which require a priori data to be defined. Due to the long life of agricultural machinery including tractors, it is difficult to determine the actual times-to-failure. By definition, these are renewable objects that can be repaired more than once. That is why, for example, agricultural tractors tend to be used longer than predicted by various methodologies for their operating time. According to the IBMER methodology, the operation time of farm tractors in Poland is assumed [23] to be 20 years. On the other hand, according to Agroscope--the Swiss Confederation's center of excellence for agricultural research--the impairment period of a farm tractor is 15 years [24]. A full service history enables an a posteriori quantification of the reliability of agricultural machinery and tractors with dedicated methods and techniques based on assumptions of the classical theory of reliability. It distinguishes only two states describing the condition of a technological device: able or unable to perform the tasks for which it was designed [25; 26]. Due to the fact that manufacturers consider data on the failure rate of agricultural equipment to be sensitive, it is difficult to access them. Without actual, credible and reliable data, it is impossible to create reliability rankings, which are so important for agricultural practice. In order to overcome the problem of a posteriori data availability, data generation procedures using a number of generators are applied, and to check their usability, their quality is compared to incomplete data. The software-generated numbers are not truly random because the computer uses an algorithm based on distribution, and they are not secure because they rely on deterministic, predictable algorithms.