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Nick Aschberger, Trackside Intelligence: Complexities of wayside detection equipment data management

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Information about Nick Aschberger, Trackside Intelligence: Complexities of wayside...
Business & Mgmt

Published on March 12, 2014

Author: informaoz

Source: slideshare.net

Description

Nick Aschberger, Software Development Manager, Trackside intelligence delivered this presentation at the 2013 Heavy Haul Rail conference. The highly anticipated event is the annual meeting place for mining and rail representatives from around the country to discuss all the latest rail projects in the heavy haul sector. For more information about the event, please visit the conference website: http://www.informa.com.au/hhrail14
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Complexities of wayside detection equipment data management Nick Aschberger Software Development Manager

Commercial in Confidence This is my first time speaking at a conference So. This will go one of two ways.

Commercial in Confidence

Commercial in Confidence Niiiiiiiiiiiice

Commercial in Confidence Here’s what we’ll talk about 1. A bit of background - introduction to wayside condition monitoring devices. 2. What the data is used for and why is this complex? 3. Simple software engineering approaches to handling this data and reducing error.

Commercial in Confidence Wayside devices - background A train goes by a wayside con-mon system. Data for each component passing by (depending on the device type) is recorded. Many different devices in heavy haul. Data is used by maintenance planners.

Commercial in Confidence Device timeline 1950/60s HBDs & DEDs 1980s WILD & WID Hot/Cold Wheels 1990s Bogie Geometry 2000s RailBAM Wheel Profile

Commercial in Confidence HBD and DED

Commercial in Confidence WILD

Commercial in Confidence RailBAM

Commercial in Confidence Wheel Profile

Commercial in Confidence But Wait! There’s more! Bogie Geometry Hi-Speed imaging and recognition (brake pads & shoes, couplers, wedges, etc) Noise/Squeal (EPA) Steak knives!

Commercial in Confidence When planning maintenance? Prioritization Condition monitoring data Existing work requests Wagons that are maintained

Commercial in Confidence So what’s so hard about that? So you just incorporate some con- mon data into your maintenance process. Easy Peasy.

Commercial in Confidence It’s not easy. If it were easy, I would be unemployed and my children would starve.

Commercial in Confidence

Commercial in Confidence There are a number of reasons it’s not easy. So let’s discuss.

Commercial in Confidence #1 - Devices are all different Each vendor supplies a system. Each vendor has different ways of retrieving and interpreting data. The planner needs expertise in multiple systems. It takes hours (days) to interrogate and compile that data.

Commercial in Confidence #2 – The data contains errors. The “dirty secret” of condition monitoring devices. Devices can make errors – resulting in errors in maintenance planning, or time is spent compensating/analysing.

Commercial in Confidence Errors come from many situations HEAT DUST CALIBRATION WEATHER SUNLIGHT (Birds, Snails, Plastic Bags)

Commercial in Confidence The maintenance system too! If you are doing the job of planning for rolling stock maintenance, then work requests in the maintenance system are an input to this decision as well. As are maintenance campaigns. But the same problem exists! Data can be entered late, incorrectly or not at all.

Commercial in Confidence #3 - Data association problems All modern systems use AEI readers (vehicle AVI tags) to associate readings with vehicles, axles and components. But, this is not 100% fool-proof. (Bad reads, Failed tags, Intermittent tags, Wrong programming)

Commercial in Confidence AVI Tag Statistics (Sample data aggregated over our heavy haul customers) Total AVI tag read attempts 25,603,252 Total missed AVI tag reads 1,296,702 or 5.06 % Total missed due to failed/missing tags on vehicles 1,117,062 or 4.36 % Total missed due to other reasons (tag reader failure, environment) 179,640 or 0.7%.

Commercial in Confidence #4 – How to choose one job over another? Which is worse? (Example) 1.A medium level wheel flat alert, or 2.Two low level acoustic bearing faults alerts? Unfortunately we can’t tell you this! And it changes over time anyway!

Commercial in Confidence The NET effect? If you’re not making optimal decisions, then you’re either: • Over-maintaining: Extra cost • Under-maintaining: Increasing production delay risk

Commercial in Confidence OK, then.

Commercial in Confidence It’s not so bad For 80% of decisions, things work pretty well, without even thinking about this stuff. But… we need to be better than that.

Commercial in Confidence It’s not so bad For 80% of decisions, things work pretty well, without even thinking about this stuff. But… we need to be better than that.

Commercial in Confidence Simple software engineering concepts We can apply some well known and reasonably straight forward software engineering concepts to this problem. But be aware there is “No silver bullet”. (Fred Brooks, 1986, No Silver Bullet — Essence and Accidents of Software Engineering)

Commercial in Confidence Get everything in one place The first (obvious?) thing to do is to get everything in one place – condition monitoring and maintenance data. This removes the expensive effort performed by the planner. NOTE: That database will have a HUGE amount of data in it.

Commercial in Confidence Data warehousing A specific structure of database to:  Handle big data (a lot of history).  Doesn’t need to be used for real time alerting.  Optimized for big queries over large data sets, not individual transactions.

Commercial in Confidence Data warehousing Transactional (Traditional) Database Data warehouse Data sources Operations, Users, Transactions Other databases & systems Purpose To control/capture data transactions as they happen To consolidate data for analysis and reporting over time Features & Structure Optimized for transaction processing. Fast inserts/updates per transaction. Normalized structure. Fewer database indexes. Optimized for querying & reporting. Faster queries, over larger sets of data De-normalized structure. More database indexes.

Commercial in Confidence Just doing this 1. Reduction in effort – just consolidating the data in one spot saves ~50% of the planners time. 2. Much more complex queries can be defined, specific for each business need. 3. Historical repository for analysis.

Commercial in Confidence Data cleansing. Just chuck it out. Another (obvious?) technique. We have lots of data anyway. Per-instrument rules applied. This applies to maintenance data too!

Commercial in Confidence Example – removing a wheel profile error

Commercial in Confidence Rates of data removed Device type # Train reads # invalid train reads % invalid train reads Laser wheel profile 3399 417 12.2% RailBAM 11010 0 0% Wheel Impact Load detector 7315 3 0.04% Hot Box Detector 3497 21 0.6% *Laser devices are heavily effected by dust and sunlight in heavy haul *RailBAM cleans its own data

Commercial in Confidence Total Records 27062 Records deleted as invalid entries by a user (open for a period of time) 462 1.7% Records in an invalid state 493 1.82% Records that are in a “work started” state for more than 6 weeks 44 0.16% Records requesting work that is already in the system (duplicates) 1901 7%. Maintenance data errors

Commercial in Confidence Trend based decisions Now we leverage the power of the consolidated data warehouse. To make informed decisions no individual measurement can be relied on. Tools that facilitate trending are built on top of the warehouse.

Commercial in Confidence Trending tools Simple: • Counts of events • Database queries that aggregate information for each component • Running scores that are calculated by the warehouse • Visualisation tools, where required.

Commercial in Confidence Trend graph example

Commercial in Confidence Conclusions (1) 1. Simple (but not easy) software techniques can be applied to give us higher confidence in our decision making. 2. Build a data warehouse containing both condition monitoring data and maintenance information. 3. The presence alone of the data warehouse itself greatly reduces time spent in maintenance planning.

Commercial in Confidence Conclusions (2) 4. Apply the principles of Data Cleansing, and Trending of measurements to the warehouse, to significantly reduce errors in maintenance planning. 5. This in turn reduces cost: • Reduction in over-maintenance, or • Reduction in production delay risk.

Commercial in Confidence We made it

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