Theory earns its keep when it survives contact with a real machine. This chapter takes eight systems, one from each industry the platform serves, and works the reliability question on each of them end to end: which analysis technique the system actually calls for, why that one and not another, the data, the formulas, the arithmetic, and what the answer tells a designer to change.
The eight systems are the same in every concept topic of this row. The aircraft electrical system worked here for reliability is the one worked in maintainability for its repair times, in availability for its dispatch, in safety for its catastrophic failure condition and in testability for its built-in test coverage. Read a column and you learn one property across eight industries; read a row and you watch one machine reveal its five RAMS faces. Every page ends by handing you sideways to the same machine's next property. All numbers are invented for teaching, chosen to be plausible and to make the arithmetic legible; none is field data from any real product.
The technique is a choice, not a default
The single most useful thing this chapter does is refuse to apply one model everywhere. A constant failure rate is right for mature electronics in their useful life and wrong for anything that wears; field data beats prediction the moment enough of it exists; and a closed form beats a simulation whenever one is available and defensible. Each page names its technique and justifies it, then says what a different technique would have given and why it was rejected.
| Industry | System | Technique this system calls for |
|---|---|---|
| Defence and aerospace | Transport aircraft AC electrical power | Part-stress prediction, constant λ, into an RBD and a fault tree |
| Railway | Wayside level-crossing controller | Mixed: constant λ for electronics, Weibull fitted from field returns for the barrier drives |
| Space systems | Satellite attitude control | Monte Carlo simulation, because a k-out-of-n of ageing items with a reconfiguration step has no usable closed form |
| Automotive | Electric vehicle brake system | Maximum-likelihood Weibull from warranty returns, with the surviving fleet carried as suspensions |
| Energy and resources | Gas compressor train | Three regimes at once: Weibull age replacement, Markov for the repairable pump pair, voting arithmetic |
| Medical devices | Infusion pump fleet | Field-data fitting with the mixed population separated before anything is fitted |
| Electronics and high tech | Storage array controller pair | FIT parts-count prediction, the page where constant λ is correct and is defended |
| Nuclear | Emergency diesel generator train | Per-demand binomial plus per-hour exponential, measured from surveillance, with a chi-square bound |
What the eight rows have in common
Read down the column and a pattern emerges that no single example shows. In every one of these systems the raw failure-rate table was the easy part and the least decisive: what determined the answer was the model chosen for the physics, the structure the rates were poured into, the direction the items fail in, whether the redundancy was genuinely independent, and whether the data was allowed to overrule the prediction.
The aircraft's answer came from redundancy topology, the crossing's from a shape parameter that made preventive replacement worthwhile, the satellite's from a rising hazard that no exponential reproduces, the vehicle's from a percentile requirement its average passed and its shape failed, the compressor's from three coexisting regimes needing three techniques, the pump fleet's from a mixture that hid its own structure, the array's from the limits of q², and the diesel's from a common cause that outweighed the second train.
That is the argument for reading the other four columns. A reliability number that is correct in isolation can still be the wrong number to have improved, and the properties in maintainability, availability, safety and testability are where that judgment is actually made.