Not All Data Is Created Equal

Indian Manufacturing News

July 31, 2018

Going beyond the initial collection of connected device or sensor data to derive actionable insights is essential in todays highly competitive, digital business landscape. For example, in order to optimize operational efficiency, satisfy customer demand and maintain security, manufacturing organizations must continually monitor and understand all of the data their machines are constantly producing.


Depending on the specific vertical of a manufacturing organization, the specific use case for what can be done with machine data varies significantly. For many manufacturers, operational processes are tightly controlled, so leveraging machine data to gain insights is fairly straightforward. For those on the light industrial (e.g. agriculture or food processing) and the heavy industrial (e.g. oil, mining or steel) sides, however, processes are perpetually in flux, making it difficult to do anything other than simply collect machine data.


Thankfully, there are tactics light and heavy industrial manufacturers can employ to put their unique breed of machine data to work. Consider these four best practices to improve operational efficiencies, reduce costly downtime and even implement predictive maintenance initiatives:

  1. Assess the data quality.

  2. As a first step, take a step back and ask, Do we have enough data here to do anything meaningful? Often, light and heavy industrial manufacturers only have a single source of data, such as a vibration sensor, or perhaps their machines are leased and therefore frequently changing location, providing little consistent data. Another common issue is not having existing datasets that indicate patterns of machine failure, as catastrophic machine failures usually occur so rarely.


    To make existing data more workable, try building out a wider dataset by incorporating additional, similar machines and looking at a shorter time period. In instances of single sensor sources, lean on subject matter experts to decipher patterns and define phases of machine cycles and performance for each. To establish a more robust picture of instances of machine failure, introduce a wider array of sensors or data sources. For instance, it might help to incorporate ERP data to better quantify outputs or leverage other machine data to build a bigger picture of a production line.

  3. Examine the data collection process.

  4. Once the quality of the data has been assessed, its time to analyze the data collection process and recognize any limitations it might produce. For example, for a light industrial agricultural manufacturer versus a heavy industrial steel manufacturer, data collection is going to look very different. Its not cost effective to create a network of sensors to cover a 1,000-acre farm, so chances are theyll need to rely on sensor stations and have tractors pass by to harvest data in a batch process manner. In a steel factory, however, machines are kept close together, so harvesting data is easier. There are other risks heavy industrial manufacturers will need to consider, such as network bandwidth limitations, security breaches, or general interference from the metal on the factory floor.

  5. Determine the data consistency and velocity.

  6. In conjunction with examining the data collection process and any complications it might introduce, its important to recognize the speed and quality at which data is being processed. For instance, in scenarios where data is being pulled in from different locations, the data quality will likely be highly inconsistent. Consider the light industrial agricultural manufacturer example: their data is unusable for roughly half the year due to the seasonality of their business. And because the business cycle for a farm is measured in months, it will require years of data collection to build a sufficiently full picture. For heavy industrial manufacturers, however, the data velocity will likely be higher, and depending on the specific vertical of heavy manufacturing, the data consistency could be fairly reliable.

  7. Confirm the data value.

  8. One of the most important steps in preparing to leverage machine data is confirming its value. More data is not always better, especially considering the cost of acquiring and storing large amounts of data. For example, a well failure in an oil field and unplanned downtime with a CNC machine are going to produce very different economic impacts. A CNC machine may be generating thousands of data readings per second, with a downtime event only resulting in $5-10K in costs, while an oil field well may be generating a fraction of that data (say, 5-10 readings every few minutes), with a downtime event costing upwards of $250K per hour. An economic case can be made for both situations. However, using data to anticipate any future outages of the oil field well is clearly the more cost-effective scenario.


No matter the datas source(s), collection process, consistency or value, theres a path forward for both light and heavy industrial manufacturers that seek to make their existing machine data actionable. The key is recognizing the datas variables, rather than just blindly capturing all data and expecting instantly productive insights. Work to define specific use cases for your data, and lean on any available subject matter experts in your organization to identify the most promising datasets and patterns. In doing so, manufacturers can obtain a more realistic and full view of their machine data, and apply that intelligence to improve their business operations in a scalable, cost-effective manner.


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Cooking Oil Coating Prevents Bacteria From Growing on Food Processing Equipment, Study Suggests

Indian Health Care News

July 31, 2018

Many foods produced on an industrial scale include raw ingredients mixed together in enormous stainless-steel machines that can be difficult to clean. With repeated use, equipment surfaces get minute scratches and grooves, providing bacteria and biofilms the perfect place to hide. While surface scratches may appear small to the naked eye, they are like a canyon to bacteria, which are only a few micrometers in size. Surface-trapped food residue and bacteria then increase the risk of contamination from microorganisms such as Salmonella, Listeria and E. coli.


Professor Ben Hatton of the University of Torontos Department of Materials Science & Engineering, Dr. Dalal Asker and Dr. Tarek Awad research cheaper, safer and more effective ways to prevent bacteria thriving inside these machines. This minimizes the risk of cross contamination, which can lead to food-borne disease. Their team have proposed a simple new solution: trapping a thin layer of cooking oil at the metal surface to fill in microscopic scrapes, cracks and fissures and create a barrier to bacterial attachment.


They found that this solution resulted in a 1,000x reduction in bacterial levels inside the industrial machines tested. Their work is recently published in the journal ACS Applied Materials & Interfaces.


Coating a stainless-steel surface with an everyday cooking oil has proven remarkably effective in repelling bacteria, says Hatton who collaborated on the project with AGRI-NEO, an Ontario seed processing company looking for a solution to a common problem in its industry. The oil fills in the cracks, creates a hydrophobic layer and acts as a barrier to contaminants on the surface.


This simple and cost-effective alternative builds on the Slippery Liquid-Infused Porous Surfaces (SLIPS) principle, initially developed at Harvard to trap lubricant layers into a surface microstructure and create slippery, non-wetting and non-adhesive properties. Cooking oils such as olive, corn or canola also provide a safer option for cleaning food-processing equipment than the harsh chemicals and disinfectants that are typically used. The sheer size of the machines makes it harder for cleaning materials to do a thorough job, and leftover bacteria can build up resistance to the cleaning agents. Hattons method of filling the scratches with oil prevents bacteria from settling and essentially cleans the surface without leaving chemical residues on the stainless-steel surface.


Contamination in food preparation equipment can impact individual health, cause costly product recalls and can still result after chemical-based cleaning occurs, says Hatton. The research showed that using a surface treatment and a cooking oil barrier provides greater coverage and results in 1,000 less bacteria roaming around.


The Hatton research group continues to test new combinations of oils, foods and biofilm types to increase the efficiency of the bacteria barriers. They will also explore options of using this method in developing countries to minimize bacterial infection and improve mortality rates.


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Toyota to Expand Scale, Shrink Cost of Hydrogen Vehicles

China Daily

July 30, 2018

Toyota Motor Corp. is doubling down on its investment in hydrogen fuel cell vehicles, designing lower-cost, mass-market passenger cars and SUVs and pushing the technology into buses and trucks to build economies of scale.


As Toyota cranks up improvements for the next generation of its Mirai hydrogen fuel cell vehicles, or FCV, expected in the early 2020s, it is hoping to prove critics wrong; most rival automakers and industry experts have dismissed such plans as commercially unviable.


The maker of the Prius, the worlds first mass-produced eco-friendly gasoline-hybrid car in the 1990s, says it can popularize FCVs, in part by making them cheaper.


Were going to shift from limited production to mass production, reduce the amount of expensive materials like platinum used in FCV components, and make the system more compact and powerful, said Yoshikazu Tanaka, chief engineer of the Mirai.


The company is planning a phased introduction of other FCV models, including a range of SUVs, pickup trucks and commercial trucks, beginning around 2025, a source with knowledge of the automakers plans said.


The automaker declined to comment on specific future product plans. But it has developed FCV prototypes of small delivery vehicles and large transport trucks based on models already being driven, as Tesla Inc. develops a battery-operated commercial semi truck from the ground up.


Were going to use as many parts from existing passenger cars and other models as possible in fuel cell trucks, said Ikuo Ota, manager of new business planning for fuel cell projects at Toyota. Otherwise, we wont see the benefits of mass production.


The company is also betting on improved performance. Toyota wants to push the driving range of the next Mirai to 700-750 kilometers from around 500 kilometers, and to hit 1,000 kilometers by 2025, a separate source said.


Driven by the belief that hydrogen will become a key source of clean energy in the next 100 years, Toyota has been developing FCVs since the early 1990s.


Hydrogen is the most abundant element in the universe and stores more energy than a battery of equivalent weight.


The Mirai was the worlds first production FCV when it was launched in 2014. But its high cost, around $60,000 before government incentives, and lack of refueling infrastructure have limited its appeal. Fewer than 6,000 have been sold globally.


LMC Automotive forecasts FCVs will make up only 0.2% of global passenger car sales in 2027, compared with 11.7% for battery EVs. The International Energy Agency predicts fewer FCVs than battery-powered and plug-in hybrid electric vehicles through 2040.


Many automakers, including Nissan Motor Co. and Tesla, see battery-powered cars as a better, zero-emission solution to gasoline engines. Only a handful, including Honda Motor Co. and Hyundai Motor Co., produce FCVs.


But people familiar with Toyotas plans said the automaker thinks demand will perk up as more countries, including China, warm to fuel cell technology. The company also sees FCVs as a hedge against a scarcity of key EV battery materials such as cobalt.


Hand built


For now, Mirais are assembled by hand at a plant in Toyota City, where 13 technicians push partially constructed units into assembly bays for detailed inspections. This process yields just 6.5 cars a day, a sliver of Toyotas average domestic daily production of about 13,400 vehicles.


Strategic Analysis Inc., which has analyzed the cost of FCVs including the Mirai, estimates that it costs Toyota about $11,000 to produce each of its fuel cell stacks, by far the vehicles most expensive part.


Toyota has been building up production capacity to change that, as it expects global FCV sales climb to 30,000 units annually after 2020 from about 3,000. Strategic Analysis estimates that would allow Toyota to reduce costs to about $8,000 per stack.


It has already begun to use parts developed for the Mirai in other models, such as the fuel cell stack, which is used in Kenworth freight trucks being tested in California, the Sora FC bus it released in Japan in March and the delivery trucks it will test with 7-Eleven stores in Japan next year. It will be difficult for Toyota to lower FCV production costs if it only produces the Mirai, the first source told Reuters on condition of anonymity as he was not authorized to speak publicly about the issue.


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Southwest: Other Carriers Finding Cracked Engine Fan Blades

Canadian Press

July 30, 2018

By David Koenig


A small number of fan blades with cracks like those blamed for a fatal accident on Southwest Airlines have been found at other airlines, and the engine maker is considering recommending more frequent inspections.


A spokesman for General Electric, one of two companies that owns the engine manufacturer, said Friday that a handful of problematic fan blades have been removed during stepped-up inspections that followed the Southwest accident in April.


Southwests chief operating officer, Mike Van de Ven, said he knows of maybe four or five reports of cracked fan blades at other carriers. Neither Van de Ven nor GE identified the airlines.


A spokesman for the National Transportation Safety Board declined to comment on the statements by Southwest and GE.


The blades are being analyzed as part of the NTSBs investigation of the accident in which a woman died after being pushed partly out of a broken window as her plane cruised 32,000 feet above the ground. The safety board has scheduled a hearing on the accident for Nov. 14.


The NTSB said earlier last week that the hearing will examine fan blade design and inspections. The board will also look at measures to prevent broken parts from becoming deadly shrapnel, as happened on the Southwest flight.


That engine was made by CFM International, a joint venture of GE and Frances Safran SA. GE spokesman Rick Kennedy said about 150,000 blades were inspected after the Southwest accident. The inspections focused on blades from engines that had made a high number of flights and were considered at greater risk of metal fatiguethe formation of invisible cracks from wear.


Kennedy said that in the 21 years since the CFM56-7B engine went into service there have been only two incidents in which a fan blade broke.


We believe with the knowledge we have gained through our inspections over the past two years and the aggressive manner in which the blades are being tracked and inspected, we have a strong process for ensuring flight safety for the fleet, he said.


Van de Ven said Thursday that GE told Southwest it is considering recommending that airlines inspect and lubricate fan blades every 1,600 to 1,800 flights instead of every 3,000 flights. CFM International suggested the 3,000-flight maintenance schedule after the accident. Van de Ven said Southwest checked 17,000 blades in 30 days and will recheck them every 1,600 flights.


Investigators believe that a broken fan blade triggered a catastrophic breakup of one of the engines on a Southwest jet as it flew from New York to Dallas on April 17. Jennifer Riordan, a mother of two, was sitting next to a window that was shattered by engine debris. The bank executive from Albuquerque, New Mexico, died of blunt-force injuries. The pilots made a safe emergency landing in Philadelphia.


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