Next, let us find out about two common challenges that designers face
when designing their sensors into their products.
There are more than just two challenges we have to overcome,
but these two seem to be prevalent due to the technology train
in designing portable battery-operated devices.
The Internet of Things plays a big part in this technology train too.
Besides overcoming design challenges, we surely see sensor failures.
Let's see what can happen when a sensor fails,
and let us go through some steps in troubleshooting a failed sensor.
Many smart or intelligent products in the market
have multiple types of sensors packed into a small package.
The figure on the right shows multiple sensors crammed into a small smartphone.
A typical car today has hundreds of sensors packed into it.
These sensors can play a critical role in the functioning and safety of a driver.
For example, a car has a yaw rate sensor,
accelerometer, wheel speed sensor, steering angle sensors,
and more to control its motion stability.
With multiple sensors crammed into small spaces,
there is a need for extremely versatile test instruments.
DMMs today have a built-in signal conditioning circuit
to measure various types of sensors.
They can generally measure all types of thermocouple sensors.
LTE sensors, thermistor sensors, diode,
capacitive, and resistive bay sensors.
Furthermore, there are advanced DMMs that are programmable to add gain
and offsets to measure custom sensors.
There are many features in these advanced DMMs,
such as wide test ranges, 2-wire/4-wire input capability,
data logging, digitizer, and more that helps a product designer
make more sense of the measurements.
Another key challenge for sensors, particularly the sensors
that require power to operate, is the power consumption efficiency.
Many portable devices need batteries to operate.
These portable devices need to go into hibernation mode when not in use,
and sensors need to perform at extremely low current.
The devices must then automatically go into inactive mode during operation.
The figure here shows the power consumption of a device
at 1 to 10 µA while in hibernation mode and at 30 to 40 mA while in active mode.
You really need a DMM with a wide dynamic range to measure the signal.
DMMs today have wider measurement ranges.
Some DMMs can measure from microamps to amps.
You can seamlessly measure and monitor the current measurements
in autoranging changing mode.
Another solution for modern DMMs is the digitizer feature.
You can set a trigger to capture transience of power consumption changes
from hibernation mode to active mode.
The figure here shows the many types of sensors
that can be wired to a control system and displayed for specific applications.
When a sensor fails, there are a few scenarios that can happen.
There can be more than what I'm describing right now.
The first one, the sensor or the control system, is smart enough to detect a fault
and provide a warning to the user.
Some control systems do have redundancies built in to be able to do this.
Second is there is an intermittent failure,
and the control system may or may not provide a warning.
The application may be partially functioning or not functioning during this scenario.
The third is there is a complete failure in the application,
and the system does not know or provide feedback to the users.
The impact of the application failure depends on the criticalness
of the feedback to the control system.
If it is critical, normally redundancy,
or some form of intelligent sensor management needs to be implemented.
There are now intelligent sensors in the market that can self-test, self-validate,
and self-adapt based on the external environment.
When a sensor fails, someone has to troubleshoot
what went wrong with the sensors.
It is very reasonable to determine how the sensor fails
and the potential cause of it to fail.
This will help to improve your overall system over time.
Here are the steps to troubleshoot a sensor.
Step one is a very important step.
It is the preparation part of the troubleshooting of a sensor.
You need to consider all the tools required to get access to the faulty sensor in question.
You will need the right electrical probing instrument
to validate the measurements seen by the control system.
Step two is the consideration needed in troubleshooting a sensor.
On the previous slide, you have seen how the sensor is linked
to the control system and the display.
This is considered the sensor path.
You can use a DMM to perform continuity or a resistance check
to isolate the path where the problem is located.
Step three is when you have already isolated the failure.
You will need to determine how the sensor failed and the cause of its failure.
The figure on the right-hand side shows some examples
of how the sensor fails and its potential causes.
Step four, it is important to analyze the failures
and provide recommended solutions.
Record your corrective actions taken.
Over time, you can analyze the effectiveness of your actions taken
and provide a list of best practices.
You can prevent certain sensor failures with your new designs.
Sensor characterization is necessary to ensure a guaranteed level of readout accuracy
over various operating conditions.
Sometimes sensors are exposed to and operate in a range of temperatures,
levels of humidity, or even at different atmospheric pressures.
Manufacturers need to characterize the sensors they produce
to analyze their dynamic range linearity, bandwidth, response time,
noise, and stability to name a few.
Publishing sensors characterize information allowing users to know how to use sensors
in their applications effectively.
The figure on the left shows an example of a three-dimensional plot
of a characterized sensor.
For example, the Z-axis represents the sensor readout, such as the voltage.
The X and Y-axis are the various external conditions the sensor is subject to,
such as input frequency of various operating temperatures,
humidity of the environment.
Users can analyze the readout information of the characterized sensors
to develop error correction algorithms to improve the sensor accuracy
and design more stable products.
During a sensor characterization process,
one of the areas that can affect measurement accuracy is the offset error.
The error occurs when a measure value appears
when there should not be a value.
The figure on the left shows the characteristics of two measurements of a linear sensor.
The red line was an initial measurement when X equals 0, F(x) was also 0.
After many measurements, the sensors may indicate zero offset drift
perhaps due to temperature or circuit component drifts.
The blue line shows a subsequent end measurement
where an offset error is seen.
Some DMMs have an auto zero function to fix this offset error.
With auto zero, the DMMs internally measure the offset following each measurement.
It then subtracts the measurement from the preceding reading.
This prevents offset voltages present in the DMM from affecting measurement accuracy.
Noise affects sensors,
especially at the bottom half of the output dynamic range.
There are temperature noise, audible noise,
electromagnetic noise, and more in our environment.
Sensor output signals are affected by surrounding noise
when they measure at their lowest dynamic range.
The figure on the right shows an oscilloscope capture
of an AC signal with noise riding on the signal.
To achieve accurate sensor measurements at the sensor's lowest dynamic range,
you need to remove the noise equation.
Good DMMs have a built-in, integrating-noise, power-line cycle function,
or NPLC, where you can easily set the integrating time
to remove noise from power lines.
Some DMMs also have built-in smoothing filters used to reduce random, average noise.
If you measure AC signals, some DMMs have AC filters
to optimize low-frequency accuracy.