# FAS Simulator A modular benchmark simulator for Flexible Assembly Systems (FAS) or Flexible Manufacturing Systems (FMS). Licence: WTFPL This project includes a SimPy discrete event simulator simulating a plausible Flexible Assembly System documented in the [documentation/FAS-Simulator.pdf](https://github.com/keskival/FAS-Simulator/raw/master/documentation/FAS-Simulator.pdf). See Executive summary poster in: [documentation/ExecutiveSummary.pdf](https://github.com/keskival/FAS-Simulator/raw/master/documentation/ExecutiveSummary.pdf). It contains several pre-configured scripts for different kinds of production runs, named: `run*.py` See Python dependencies from `requirements.txt`. For plotting things you need Octave and epstool. Example: Running a simple simulation with a simulated wear and tear fault: `./run_easy_with_wear_and_tear_fault.py` Running the simulations produces output to the STDOUT, but the actual output is written as JSON to [output.json](https://github.com/keskival/FAS-Simulator/blob/master/data/output_easy_with_wear_and_tear_fault.json). The output contains a sequence of events with timestamps. Additionally, there are several `*.m` files and `*.sh` files in `utils` directory to create different kinds of visualizations out of this JSON output using Octave and ffmepg. The `data.mat` file for the Octave scripts is created using `./utils/output_to_octave.py` from `output.json` to `data.mat`. There is a baseline implementation using Hidden Markov Models for anomaly detection in: [fas_hmm](https://github.com/keskival/fas_hmm) There is also a related FAS-Tensorflow project (private at the moment) that is an implementation that extracts process model features from inputs generated by this project using deep learning methods, and uses those for anomaly detection. This is about a novel deep learning architecture which leverages interlaced sequential symmetry in the data. ## The Default Assembly Process Being Simulated Step | Description | Duration | Log messages --- | --- | --- | --- 1 | Crane | 30 s | Going forward, stopping, going back, stopping 2 | Manual inspection | 37 s | OK pressed, queue alarm 3 | Conveyor | 30 s | To station, stop, to next station 4 | Bowl feeder gives components | 5 s | Given 5 | Add components | 21 s | OK pressed, queue alarm 6 | Conveyor | 30 s | To station, stop, to next station 7 | Bowl feeder gives components | 10 s | Given 8 | Add components | 34 s | OK pressed, queue alarm 9 | Conveyor | 30 s | To station, stop, to next station 10 | Crane with subassembly A | 10 s | Going forward, stopping, going back, stopping 11 | Combine with subassembly A | 34 s | OK pressed, queue alarm 12 | Conveyor | 30 s | To station, stop, to next station 13 | Conveyor with subassembly B | 10 s | To station, stop 14 | Combine with subassembly B | 35 s | OK pressed, queue alarm 15 | Conveyor | 30 s | To station, stop, to next station 16 | Bowl feeder gives components | 5 s | Given 17 | Conveyor with cover | 10 s | To station, stop 18 | Add cover and bolts | 76 s | OK pressed, queue alarm 19 | Conveyor | 30 s | To station, stop, to next station 20 | Tighten the bolts | 28 s | OK pressed, queue alarm 21 | Conveyor | 30 s | To station, stop, to next station 22 | Conveyor with subassembly C | 10 s | To station, stop 23 | Combine with subassembly C | 60 s | OK pressed, queue alarm 24 | Conveyor | 21 s | To station, stop, to next station 25 | Tighten the bolts | 16 s | OK pressed, queue alarm 26 | Conveyor | 21 s | To station, stop, to next station 27 | Bowl feeder gives components | 5 s | Given 28 | Add components | 11 s | OK pressed, queue alarm 29 | Conveyor | 21 s | To station, stop, to next station 30 | Tighten the bolts | 32 s | OK pressed, queue alarm 31 | Conveyor | 21 s | To output gate ## Events Event id | Event name --- | --- 0 | TICK 1 | CONVEYOR1 CONVEYOR_GATE 2 | CONVEYOR2 CONVEYOR_GATE 3 | CONVEYOR3 CONVEYOR_GATE 4 | CONVEYOR4 CONVEYOR_GATE 5 | CONVEYOR5 CONVEYOR_GATE 6 | CONVEYOR6 CONVEYOR_GATE 7 | CONVEYOR7 CONVEYOR_GATE 8 | CONVEYOR8 CONVEYOR_GATE 9 | CONVEYOR9 CONVEYOR_GATE 10 | CONVEYOR10 CONVEYOR_GATE 11 | CONVEYOR11 CONVEYOR_GATE 12 | CONVEYOR_INPUT_SUBASSEMBLY_B CONVEYOR_GATE 13 | CONVEYOR_INPUT_SUBASSEMBLY_C CONVEYOR_GATE 14 | BOWL1 GIVEN 15 | BOWL2 GIVEN 16 | BOWL3 GIVEN 17 | BOWL4 GIVEN 18 | CRANE1 FORWARD 19 | CRANE1 BACKWARD 20 | CRANE1 STOP 21 | CRANE_INPUT_SUBASSEMBLY_A FORWARD 22 | CRANE_INPUT_SUBASSEMBLY_A BACKWARD 23 | CRANE_INPUT_SUBASSEMBLY_A STOP 24 | MANUAL_INSPECTION QUEUE_ALARM 25 | MANUAL_INSPECTION OK 26 | MANUAL_ADD_COMPONENTS1 QUEUE_ALARM 27 | MANUAL_ADD_COMPONENTS1 OK 28 | MANUAL_ADD_COMPONENTS2 QUEUE_ALARM 29 | MANUAL_ADD_COMPONENTS2 OK 30 | MANUAL_COMBINE_SUBASSEMBLY_A QUEUE_ALARM 31 | MANUAL_COMBINE_SUBASSEMBLY_A OK 32 | MANUAL_COMBINE_SUBASSEMBLY_B QUEUE_ALARM 33 | MANUAL_COMBINE_SUBASSEMBLY_B OK 34 | MANUAL_ADD_COVER_AND_BOLTS QUEUE_ALARM 35 | MANUAL_ADD_COVER_AND_BOLTS OK 36 | MANUAL_TIGHTEN_BOLTS1 QUEUE_ALARM 37 | MANUAL_TIGHTEN_BOLTS1 OK 38 | MANUAL_COMBINE_SUBASSEMBLY_C QUEUE_ALARM 39 | MANUAL_COMBINE_SUBASSEMBLY_C OK 40 | MANUAL_TIGHTEN_BOLTS2 QUEUE_ALARM 41 | MANUAL_TIGHTEN_BOLTS2 OK 42 | MANUAL_ADD_COMPONENTS3 QUEUE_ALARM 43 | MANUAL_ADD_COMPONENTS3 OK 44 | MANUAL_TIGHTEN_BOLTS3 QUEUE_ALARM 45 | MANUAL_TIGHTEN_BOLTS3 OK ## Operating Modes There are several runs implemented: - Run without errors - Wear and tear fault, where a Conveyor is affected by a progressive delay. - Retry delay fault, where a BowlFeeder process suffers from failed feed randomly, but continues correctly when retried. ## Generating Challenge Dataset For the challenge task we create a set of clean runs, and a set of runs with errors happening at the starting point in the run. A suggested learning system can be trained or conditioned on a subset of clean runs, and tested whether it is able to discriminate between runs with errors and clean runs. Note that a system shouldn't be trained on the faulty runs, because the fault types and behaviors cannot be known in advance in practice. We first produce 10,000 runs with errors and 10,000 similar runs without errors in JSON. Do this by running: `./generate_datasets.py`. The data will be generated in JSON form, one file per run in `data/correct_runs/*.json` and `data/runs_with_errors/*.json`. This data can then be converted to numpy format by running: `./convert_datasets_to_numpy.py`. The data will be generated in numpy form, one file per run in `numpy_data/correct_runs/*.npy` and `numpy_data/runs_with_errors/*.npy`. ## Citing FAS Simulator ``` @article{keski2017simulator, title={A simulator for event-oriented data in flexible assembly system fault prediction}, author={Keski-Valkama, Tero}, journal={Procedia computer science}, volume={119}, pages={121--130}, year={2017}, publisher={Elsevier} } ``` ## Repository - [https://github.com/keskival/FAS-Simulator](https://github.com/keskival/FAS-Simulator)